Gene expression profiling of EGFR positive cancer

ABSTRACT

The present invention concerns prognostic markers associated with EGFR positive cancer. In particular, the invention concerns prognostic methods based on the molecular characterization of gene expression in paraffin-embedded, fixed tissue samples of EGFR-expressing cancer, which allow a physician to predict whether a patient is likely to respond well to treatment with an EGFR inhibitor.

This application claims priority under 35 U.S.C. § 119(e) to provisional application Ser. No. 60/427,090 filed on Nov. 15, 2002, the entire disclosure of which is hereby expressly incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention concerns gene expression profiling of tissue samples obtained from EGFR-positive cancer. More specifically, the invention provides diagnostic, prognostic and predictive methods based on the molecular characterization of gene expression in paraffin-embedded, fixed tissue samples of EGFR-expressing cancer, which allow a physician to predict whether a patient is likely to respond well to treatment with an EGFR inhibitor. In addition, the present invention provides treatment methods based on such findings.

2. Description of the Related Art

Oncologists have a number of treatment options available to them, including different combinations of chemotherapeutic drugs that are characterized as “standard of care,” and a number of drugs that do not carry a label claim for particular cancer, but for which there is evidence of efficacy in that cancer. Best likelihood of good treatment outcome requires that patients be assigned to optimal available cancer treatment, and that this assignment be made as quickly as possible following diagnosis.

Currently, diagnostic tests used in clinical practice are single analyte, and therefore do not capture the potential value of knowing relationships between dozens of different markers. Moreover, diagnostic tests are frequently not quantitative, relying on immunohistochemistry. This method often yields different results in different laboratories, in part because the reagents are not standardized, and in part because the interpretations are subjective and cannot be easily quantified. RNA-based tests have not often been used because of the problem of RNA degradation over time and the fact that it is difficult to obtain fresh tissue samples from patients for analysis. Fixed paraffin-embedded tissue is more readily available and methods have been established to detect RNA in fixed tissue. However, these methods typically do not allow for the study of large numbers of genes (DNA or RNA) from small amounts of material. Thus, traditionally fixed tissue has been rarely used other than for immunohistochemistry detection of proteins.

Recently, several groups have published studies concerning the classification of various cancer types by microarray gene expression analysis (see, e.g. Golub et al., Science 286:531-537 (1999); Bhattacharjae et al., Proc. Natl. Acad. Sci. USA 98:13790-13795 (2001); Chen-Hsiang et al., Bioinformatics 17 (Suppl. 1):S316-S322 (2001); Ramaswamy et al., Proc. Natl. Acad. Sci. USA 98:15149-15154 (2001)). Certain classifications of human breast cancers based on gene expression patterns have also been reported (Martin et al., Cancer Res. 60:2232-2238 (2000); West et al., Proc. Natl. Acad. Sci. USA 98:11462-11467 (2001); Sorlie et al., Proc. Natl. Acad. Sci. USA 98:10869-10874 (2001); Yan et al., Cancer Res. 61:8375-8380 (2001)). However, these studies mostly focus on improving and refining the already established classification of various types of cancer, including breast cancer, and generally do not link the findings to treatment strategies in order to improve the clinical outcome of cancer therapy.

Although modern molecular biology and biochemistry have revealed more than 100 genes whose activities influence the behavior of tumor cells, state of their differentiation, and their sensitivity or resistance to certain therapeutic drugs, with a few exceptions, the status of these genes has not been exploited for the purpose of routinely making clinical decisions about drug treatments. One notable exception is the use of estrogen receptor (ER) protein expression in breast carcinomas to select patients to treatment with anti-estrogen drugs, such as tamoxifen. Another exceptional example is the use of ErbB2 (Her2) protein expression in breast carcinomas to select patients with the Her2 antagonist drug Herceptin® (Genentech, Inc., South San Francisco, Calif.).

Despite recent advances, the challenge of cancer treatment remains to target specific treatment regimens to pathogenically distinct tumor types, and ultimately personalize tumor treatment in order to optimize outcome. Hence, a need exists for tests that simultaneously provide predictive information about patient responses to the variety of treatment options.

SUMMARY OF THE INVENTION

The present invention is based on findings of Phase II clinical studies of gene expression in tissue samples obtained from EGFR-expressing head and neck cancer or colon cancer of human patients who responded well or did not respond to (showed resistance to) treatment with EGFR inhibitors.

Based upon such findings, in one-aspect the present invention concerns a method for predicting the likelihood that a patient diagnosed with an EGFR-expressing cancer will respond to treatment with an EGFR inhibitor, comprising determining the expression level of one or more prognostic RNA transcripts or their products in a sample comprising EGFR-expressing cancer cells-obtained from the patient, wherein the prognostic transcript is the transcript of one or more genes selected from the group consisting of: Bak; Bclx; BRAF; BRK; Cad17; CCND3; CD105; CD44s; CD82; CD9; CGA; CTSL; EGFRd27; ErbB3; EREG; GPC3; GUS; HGF; ID1; IGFBP3; ITGB3; ITGB3; p27; P53; PTPD1; RB1; RPLPO; STK15; SURV; TERC; TGFBR2; TIMP2; TITF1; XIAP; YB-1; A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CA9; CCNA2; CCNE1; CCNE2; CD134; CD44E; CD44v3; CD44v6; CD68; CDC25B; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1 ; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; P14ARF; PAI1; PDGFA; PDGFB; PGK1; PLAUR; PPARG; RANBP2; RASSF1; RIZ1; SPRY2; Src; TFRC; TP53BP1; UPA; and VEGFC, wherein (a) the patient is unlikely to benefit from treatment with an EGFR inhibitor if the normalized levels of any of the following genes A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CA9; CCNA2; CCNE1; CCNE2; CD134; CD44E; CD44v3; CD44v6; CD68; CDC25B; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1, ITGA3; KRT17; LAMC2; MTA1; NMYC; P14ARF; PAI1; PDGFA; PDGFB; PGK1; PLAUR; PPARG; RANBP2; RASSF1; RIZ1; SPRY2; Src; TFRC; TP53BP1; upa; VEGFC, or their products are elevated above defined expression thresholds, and (b) the patient is likely to benefit from treatment with an EGFR inhibitor if the normalized levels of any of the following genes Bak; Bclx; BRAF; BRK; Cad17; CCND3; CD105; CD44s; CD82; CD9; CGA; CTSL; EGFRd27; ErbB3; EREG; GPC3; GUS; HGF; ID1; IGFBP3; ITGB3; ITGB3; p27; P53; PTPD1; RB1; RPLPO; STK15; SURV; TERC; TGFBR2; TIMP2; TITF1; XIAP; and YB-1, or their products are elevated above defined expression thresholds.

In another aspect, the present invention concerns a prognostic method comprising

(a) subjecting a sample comprising EGFR-expressing cancer cells obtained from a patient to quantitative analysis of the expression level of at least one gene selected from the group consisting of CD44v3; CD44v6; DR5; GRO1; KRT17; and LAMC2 gene or their products, and

(b) identifying the patient as likely to show resistance to treatment with an EGFR-inhibitor if the expression levels of such gene or genes, or their products, are elevated above a defined threshold. In a particular embodiment, the gene is LAMC2.

In yet another aspect, the invention concerns a method for predicting the likelihood that a patient diagnosed with an EGFR-expressing head or neck cancer will respond to treatment with an EGFR inhibitor, comprising determining the expression level of one or more prognostic RNA transcripts or their products in a sample comprising EGFR-expressing cancer cells obtained from such patient, wherein the prognostic transcript is the transcript of one or more genes selected from the group consisting of: CD44s; CD82; CGA; CTSL; EGFRd27; IGFBP3; p27; P53; RB1; TIMP2; YB-1; A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CCNA2; CCNE1; CCNE2; CD105; CD44v3; CD44v6; CD68; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DLKBLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; PAI1; PDGFA; PGK1; PTPD1; RANBP2; SPRY2; TP53BP1; and VEGFC, wherein (a) normalized expression of one or more of A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CCNA2; CCNE1; CCNE2; CD105; CD44v3; CD44v6; CD68; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; PAI1; PDGFA; PGK1; PTPD1; RANBP2; SPRY2; TP53BP1; VEGFC, or the corresponding gene product, above determined expression thresholds indicates that the patient is likely to show resistance to treatment with an EGFR inhibitor, and (b) normalized expression of one or more ofCD44s; CD82; CGA; CTSL; EGFRd27; IGFBP3; p27; P53; RB1; TIMP2; YB-1, or the corresponding gene product, above defined expression thresholds indicates that the patient is likely to respond well to treatment with an EGFR inhibitor.

In a further aspect, the invention concerns a method for predicting the likelihood that a patient diagnosed with an EGFR-expressing colon cancer will respond to treatment with an EGFR inhibitor, comprising determining the expression level of one or more prognostic RNA transcripts or their products in a sample comprising EGFR-expressing cancer cells obtained from the patient, wherein the prognostic transcript is the transcript of one or more genes selected from the group consisting of Bak; Bclx; BRAF; BRK; Cad17; CCND3; CCNE1; CCNE2; CD105; CD9; COX2; DIABLO; ErbB3; EREG; FRP1; GPC3; GUS; HER2; HGF; ID1; ITGB3; PTPD1; RPLPO; STK15; SURV; TERC; TGFBR2; TITF1; XIAP; CA9; CD134; CD44E; CD44v3; CD44v6; CDC25B; CGA; DR5; GRO1; KRT17; LAMC2; P14ARF; PDGFB; PLAUR; PPARG; RASSF1; RIZ1; Src; TFRC; and UPA, wherein (a) elevated expression of one or more of CA9; CD134; CD44E; CD44v3; CD44v6; CDC25B; CGA; DR5; GRO1; KRT17; LAMC2; P14ARF; PDGFB; PLAUR; PPARG; RASSF1; RIZ1; Src; TFRC; and UPA, or the corresponding gene product, above defined expression thresholds indicates that the patient is likely to show resistance to treatment with an EGFR inhibitor, and normalized expression of one or more of Bak; Bclx; BRAF; BRK; Cad17; CCND3; CCNE1; CCNE2; CD105; CD9; COX2; DIABLO; ErbB3; EREG; FRP1; GPC3; GUS; HER2; HGF; ID1; ITGB3; PTPD1; RPLPO; STK15; SURV; TERC; TGFBR2; TITF1; XIAP, or the corresponding gene product, above certain expression thresholds indicates that the patient is likely to respond well to treatment with an EGFR inhibitor.

In another aspect, the invention concerns a method comprising treating a patient diagnosed with an EGFR-expressing cancer and determined to have elevated normalized levels of one or more of the RNA transcripts of Bak; Bclx; BRAF; BRK; Cad17; CCND3; CD105; CD44s; CD82; CD9; CGA; CTSL; EGFRd27; ErbB3; EREG; GPC3; GUS; HGF; ID1; IGFBP3; ITGB3; ITGB3; p27; P53; PTPD1; RB1; RPLPO; STK15; SURV; TERC; TGFBR2; TIMP2; TITF1; XIAP; YB-1; A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CA9; CCNA2; CCNE1; CCNE2; CD134; CD44E; CD44v3; CD44v6; CD68; CDC25B; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DLKBLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; P14ARF; PAI1; PDGFA; PDGFB; PGK1; PLAUR; PPARG; RANBP2; RASSF1; RIZ1; SPRY2; Src; TFRC; TP53BP1; UPA; and VEGFC genes, or the corresponding gene products in the cancer, with an effective amount of an EGFR-inhibitor, wherein elevated RNA transcript level is defined by a defined expression threshold.

In yet another aspect, the invention concerns a method comprising treating a patient diagnosed with an EGFR-expressing head or neck cancer and determined to have elevated normalized expression of one or more of the RNA transcripts of CD44s; CD82; CGA; CTSL; EGFRd27; IGFBP3; p27; P53; RB1; TIMP2; YB-1; A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CCNA2; CCNE1; CCNE2; CD105; CD44v3; CD44v6; CD68; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; PAI1; PDGFA; PGK1; PTPD1; RANBP2; SPRY2; TP53BP1; VEGFC genes, or the corresponding gene products in said cancer, with an effective amount of an EGFR-inhibitor, wherein elevated normalized RNA transcript level is defined by a defined expression threshold.

In a further aspect, the invention concerns a method comprising treating a patient diagnosed with an EGFR-expressing colon cancer and determined to have elevated normalized expression of one or more of the RNA transcripts of Bak; Bclx; BRAF; BRK; Cad17; CCND3; CCNE1; CCNE2; CD105; CD9; COX2; DLKBLO; ErbB3; EREG; FRP1; GPC3; GUS; HER2; HGF; ID1; ITGB3; PTPD1; RPLPO; STK15; SURV; TERC; TGFBR2; TITF1; XIAP; CA9; CD134; CD44E; CD44v3; CD44v6; CDC25B; CGA; DR5; GRO1; KRT17; LAMC2; P14ARF; PDGFB; PLAUR; PPARG; RASSF1; RIZ1; Src; TFRC; UPA genes, or the corresponding gene products in such cancer, with an effective amount of an EGFR-inhibitor, wherein elevated normalized RNA transcript level is defined by a defined expression threshold.

The invention further concerns an array comprising (a) polynucleotides hybridizing to the following genes: Bak; Bclx; BRAF; BRK; Cad17; CCND3; CD105; CD44s; CD82; CD9; CGA; CTSL; EGFRd27; ErbB3; EREG; GPC3; GUS; HGF; ID1; IGFBP3; ITGB3; ITGB3; p27; P53; PTPD1; RB1; RPLPO; STK15; SURV; TERC; TGFBR2; TIMP2; TITF1; XIAP; YB-1; A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CA9; CCNA2; CCNE1; CCNE2; CD134; CD44E; CD44v3; CD44v6; CD68; CDC25B; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; P14ARF; PAI1; PDGFA; PDGFB; PGK1; PLAUR; PPARG; RANBP2; RASSF1; RIZ1; SPRY2; Src; TFRC; TP53BP1; UPA; VEGFC; or (b) an array comprising polynucleotides hybridizing to the following genes: CD44v3; CD44v6; DR5; GRO1; KRT17; and LAMC2, immobilized on a solid surface; or (c) an array comprising polynucleotides hybridizing to the following genes: CD44s; CD82; CGA; CTSL; EGFRd27; IGFBP3; p27; P53; RB1; TIMP2; YB-1; A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CCNA2; CCNE1; CCNE2; CD105; CD44v3; CD44v6; CD68; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; PAI1; PDGFA; PGK1; PTPD1; RANBP2; SPRY2; TP53BP1; and VEGFC, immobilized on a solid surface, or (d) an array comprising polynucleotides hybridizing to the following genes: Bak; Bclx; BRAF; BRK; Cad17; CCND3; CCNE1; CCNE2; CD105; CD9; COX2; DIABLO; ErbB3; EREG; FRP1; GPC3; GUS; HER2; HGF; ID1; ITGB3; PTPD1; RPLPO; STK15; SURV; TERC; TGFBR2; TITF1; XIAP; CA9; CD134; CD44E; CD44v3; CD44v6; CDC25B; CGA; DR5; GRO1; KRT17; LAMC2; P14ARF; PDGFB; PLAUR; PPARG; RASSF1; RIZ1; Src; TFRC; and UPA, immobilized on a solid surface.

In a further aspect, the invention concerns a method in which RNA is isolated from a fixed, paraffin-embedded tissue specimen by a procedure comprising:

(a) incubating a section of the fixed, paraffin-embedded tissue specimen at a temperature of about 56° C. to 70° C. in a lysis buffer, in the presence of a protease, without prior dewaxing, to form a lysis solution;

(b) cooling the lysis solution to a temperature where the wax solidifies; and

(c) isolating the nucleic acid from the lysis solution.

In a different aspect, the invention concerns a kit comprising one or more of (1) extraction buffer/reagents and protocol; (2) reverse transcription buffer/reagents and protocol; and (3) qPCR buffer/reagents and protocol suitable for performing the gene expression analysis methods of the invention.

In a further aspect, the invention concerns a method for measuring levels of mRNA products of genes listed in Tables 5A and 5B by quantitative RT-PCR (qRT-PCR) reaction, by using an amplicon listed in Tables 5A and 5B and a corresponding primer-probe set listed in Tables 6A-6F.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a chart illustrating the overall workflow of the process of the invention for measurement of gene expression. In the Figure, FPET stands for “fixed paraffin-embedded tissue,” and “RT-PCR” stands for “reverse transcriptase PCR.” RNA concentration is determined by using the commercial RiboGreen™ RNA Quantitation Reagent and Protocol.

FIG. 2 is a flow chart showing the steps of an RNA extraction method according to the invention alongside a flow chart of a representative commercial method.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

A. Definitions

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), and March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992), provide one skilled in the art with a general guide to many of the terms used in the present application.

One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention. Indeed, the present invention is in no way limited to the methods and materials described. For purposes of the present invention, the following terms are defined below.

The term “microarray” refers to an ordered arrangement of hybridizable array elements, preferably polynucleotide probes, on a substrate.

The term “polynucleotide,” when used in singular or plural, generally refers to any polyribonucleotide or polydeoxribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA. Thus, for instance, polynucleotides as defined herein include, without limitation, single- and double-stranded DNA, DNA including single- and double-stranded regions, single- and double-stranded RNA, and RNA including single- and double-stranded regions, hybrid molecules comprising DNA and RNA that may be single-stranded or, more typically, double-stranded or include single- and double-stranded regions. In addition, the term “polynucleotide” as used herein refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The strands in such regions may be from the same molecule or from different molecules. The regions may include all of one or more of the molecules, but more typically involve only a region of some of the molecules. One of the molecules of a triple-helical region often is an oligonucleotide. The term “polynucleotide” specifically includes cDNAs. The term includes DNAs (including cDNAs) and RNAs that contain one or more modified bases. Thus, DNAs or RNAs with backbones modified for stability or for other reasons are “polynucleotides” as that term is intended herein. Moreover, DNAs or RNAs comprising unusual bases, such as inosine, or modified bases, such as tritiated bases, are included within the term “polynucleotides” as defined herein. In general, the term “polynucleotide” embraces all chemically, enzymatically and/or metabolically modified forms of unmodified polynucleotides, as well as the chemical forms of DNA and RNA characteristic of viruses and cells, including simple and complex cells.

The term “oligonucleotide” refers to a relatively short polynucleotide, including, without limitation, single-stranded deoxyribonucleotides, single- or double-stranded ribonucleotides, RNA:DNA hybrids and double-stranded DNAs. Oligonucleotides, such as single-stranded DNA probe oligonucleotides, are often synthesized by chemical methods, for example using automated oligonucleotide synthesizers that are commercially available. However, oligonucleotides can be made by a variety of other methods, including in vitro recombinant DNA-mediated techniques and by expression of DNAs in cells and organisms.

The terms “differentially expressed gene,” “differential gene expression” and their synonyms, which are used interchangeably, refer to a gene whose expression is activated to a higher or lower level in a subject suffering from a disease, specifically cancer, such as breast cancer, relative to its expression in a normal or control subject. The terms also include genes whose expression is activated to a higher or lower level at different stages of the same disease. It is also understood that a differentially expressed gene may be either activated or inhibited at the nucleic acid level or protein level, or may be subject to alternative splicing to result in a different polypeptide product. Such differences may be evidenced by a change in mRNA levels, surface expression, secretion or other partitioning of a polypeptide, for example. Differential gene expression may include a comparison of expression between two or more genes or their gene products, or a comparison of the ratios of the expression between two or more genes or their gene products, or even a comparison of two differently processed products of the same gene, which differ between normal subjects and subjects suffering from a disease, specifically cancer, or between various stages of the same disease. Differential expression includes both quantitative, as well as qualitative, differences in the temporal or cellular expression pattern in a gene or its expression products among, for example, normal and diseased cells, or among cells which have undergone different disease events or disease stages. For the purpose of this invention, “differential gene expression” is considered to be present when there is at least an about two-fold, preferably at least about four-fold, more preferably at least about six-fold, most preferably at least about ten-fold difference between the expression of a given gene in normal and diseased subjects, or in various stages of disease development in a diseased subject.

The term “normalized” with regard to a gene transcript or a gene expression product refers to the level of the transcript or gene expression product relative to the mean levels of transcripts/products of a set of reference genes, wherein the reference genes are either selected based on their minimal variation across, patients, tissues or treatments (“housekeeping genes”), or the reference genes are the totality of tested genes. In the latter case, which is commonly referred to as “global normalization”, it is important that the total number of tested genes be relatively large, preferably greater than 50. Specifically, the term ‘normalized’ with respect to an RNA transcript refers to the transcript level relative to the mean of transcript levels of a set of reference genes. More specifically, the mean level of an RNA transcript as measured by TaqMan® RT-PCR refers to the Ct value minus the mean Ct values of a set of reference gene transcripts.

The terms “expression threshold,” and “defined expression threshold” are used interchangeably and refer to the level of a gene or gene product in question above which the gene or gene product serves as a predictive marker for patient response or resistance to a drug, in the present case an EGFR inhibitor drug. The threshold is defined experimentally from clinical studies such as those described in examples 1 and 2, below. The expression threshold can be selected either for maximum sensitivity (for example, to detect all responders to a drug), or for maximum selectivity (for example to detect only responders to a drug), or for minimum error.

The phrase “gene amplification” refers to a process by which multiple copies of a gene or gene fragment are formed in a particular cell or cell line. The duplicated region (a stretch of amplified DNA) is often referred to as “amplicon.” Usually, the amount of the messenger RNA (mRNA) produced, i.e., the level of gene expression, also increases in the proportion of the number of copies made of the particular gene expressed.

The term “diagnosis” is used herein to refer to the identification of a molecular or pathological state, disease or condition, such as the identification of a molecular subtype of head and neck cancer, colon cancer, or other type of cancer. The term “prognosis” is used herein to refer to the prediction of the likelihood of cancer-attributable death or progression, including recurrence, metastatic spread, and drug resistance, of a neoplastic disease, such as breast cancer, or head and neck cancer. The term “prediction” is used herein to refer to the likelihood that a patient will respond either favorably or unfavorably to a drug or set of drugs, and also the extent of those responses, or that a patient will survive, following surgical removal or the primary tumor and/or chemotherapy for a certain period of time without cancer recurrence. The predictive methods of the present invention can be used clinically to make treatment decisions by choosing the most appropriate treatment modalities for any particular patient. The predictive methods of the present invention are valuable tools in predicting if a patient is likely to respond favorably to a treatment regimen, such as surgical intervention, chemotherapy with a given drug or drug combination, and/or radiation therapy, or whether long-term survival of the patient, following surgery and/or termination of chemotherapy or other treatment modalities is likely.

The term “long-term” survival is used herein to refer to survival for at least 5 years, more preferably for at least 8 years, most preferably for at least 10 years following surgery or other treatment.

The term “increased resistance” to a particular drug or treatment option, when used in accordance with the present invention, means decreased response to a standard dose of the drug or to a standard treatment protocol.

The term “decreased sensitivity” to a particular drug or treatment option, when used in accordance with the present invention, means decreased response to a standard dose of the drug or to a standard treatment protocol, where decreased response can be compensated for (at least partially) by increasing the dose of drug, or the intensity of treatment.

“Patient response” can be assessed using any endpoint indicating a benefit to the patient, including, without limitation, (1) inhibition, to some extent, of tumor growth, including slowing down and complete growth arrest; (2) reduction in the number of tumor cells; (3) reduction in tumor size; (4) inhibition (i.e., reduction, slowing down or complete stopping) of tumor cell infiltration into adjacent peripheral organs and/or tissues; (5) inhibition (i.e. reduction, slowing down or complete stopping) of metastasis; (6) enhancement of anti-tumor immune response, which may, but does not have to, result in the regression or rejection of the tumor; (7) relief, to some extent, of one or more symptoms associated with the tumor; (8) increase in the length of survival following treatment; and/or (9) decreased mortality at a given point of time following treatment.

The term “treatment” refers to both therapeutic treatment and prophylactic or preventative measures, wherein the object is to prevent or slow down (lessen) the targeted pathologic condition or disorder. Those in need of treatment include those already with the disorder as well as those prone to have the disorder or those in whom the disorder is to be prevented. In tumor (e.g., cancer) treatment, a therapeutic agent may directly decrease the pathology of tumor cells, or render the tumor cells more susceptible to treatment by other therapeutic agents, e.g., radiation and/or chemotherapy.

The term “tumor,” as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.

The terms “cancer” and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Examples of cancer include but are not limited to, breast cancer, colon cancer, lung cancer, prostate cancer, hepatocellular cancer, gastric cancer, pancreatic cancer, cervical cancer, ovarian cancer, liver cancer, bladder cancer, cancer of the urinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, head and neck cancer, and brain cancer.

The “pathology” of cancer includes all phenomena that compromise the well-being of the patient. This includes, without limitation, abnormal or uncontrollable cell growth, metastasis, interference with the normal functioning of neighboring cells, release of cytokines or other secretory products at abnormal levels, suppression or aggravation of inflammatory or immunological response, neoplasia, premalignancy, malignancy, invasion of surrounding or distant tissues or organs, such as lymph nodes, etc.

The term “EGFR inhibitor” as used herein refers to a molecule having the ability to inhibit a biological function of a native epidermal growth factor receptor (EGFR). Accordingly, the term “inhibitor” is defined in the context of the biological role of EGFR. While preferred inhibitors herein specifically interact with (e.g. bind to) an EGFR, molecules that inhibit an EGFR biological activity by interacting with other members of the EGFR signal transduction pathway are also specifically included within this definition. A preferred EGFR biological activity inhibited by an EGFR inhibitor is associated with the development, growth, or spread of a tumor.

The term “housekeeping gene” refers to a group of genes that codes for proteins whose activities are essential for the maintenance of cell function. These genes are typically similarly expressed in all cell types. Housekeeping genes include, without limitation, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Cypl, albumin, actins, e.g. β-actin, tubulins, cyclophilin, hypoxantine phsophoribosyltransferase (HRPT), L32. 28S, and 18S.

B. Detailed Description

The practice of the present invention will employ, unless otherwise indicated, conventional techniques of molecular biology (including recombinant techniques), microbiology, cell biology, and biochemistry, which are within the skill of the art. Such techniques are explained fully in the literature, such as, “Molecular Cloning: A Laboratory Manual”, 2^(nd) edition (Sambrook et al., 1989); “Oligonucleotide Synthesis” (M. J. Gait, ed., 1984); “Animal Cell Culture” (R. I. Freshney, ed., 1987); “Methods in Enzymology” (Academic Press, Inc.); “Handbook of Experimental Immunology”, 4^(th) edition (D. M. Weir & C. C. Blackwell, eds., Blackwell Science Inc., 1987); “Gene Transfer Vectors for Mammalian Cells” (J. M. Miller & M. P. Calos, eds., 1987); “Current Protocols in Molecular Biology” (F. M. Ausubel et al., eds., 1987); and “PCR: The Polymerase Chain Reaction”, (Mullis et al., eds., 1994).

1. Gene Expression Profiling

In general, methods of gene expression profiling can be divided into two large groups: methods based on hybridization analysis of polynucleotides, and methods based on sequencing of polynucleotides. The most commonly used methods known in the art for the quantification of mRNA expression in a sample include northern blotting and in situ hybridization (Parker & Barnes, Methods in Molecular Biology 106:247-283 (1999)); RNAse protection assays (Hod, Biotechniques 13:852-854 (1992)); and reverse transcription polymerase chain reaction (RT-PCR) (Weis et al., Trends in Genetics 8:263-264 (1992)). Alternatively, antibodies may be employed that can recognize specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes or DNA-protein duplexes. Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression (SAGE), and gene expression analysis by massively parallel signature sequencing (MPSS).

2. Reverse Transcriptase PCR (RT-PCR)

Of the techniques listed above, the most sensitive and most flexible quantitative method is RT-PCR, which can be used to compare mRNA levels in different sample populations, in normal and tumor tissues, with or without drug treatment, to characterize patterns of gene expression, to discriminate between closely related mRNAs, and to analyze RNA structure.

The first step is the isolation of mRNA from a target sample. The starting material is typically total RNA isolated from human tumors or tumor cell lines, and corresponding normal tissues or cell lines, respectively. Thus RNA can be isolated from a variety of primary tumors, including breast, lung, colon, prostate, brain, liver, kidney, pancreas, spleen, thymus, testis, ovary, uterus, head and neck, etc., tumor, or tumor cell lines, with pooled DNA from healthy donors. If the source of mRNA is a primary tumor, mRNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples.

General methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987), and De Andres et al., BioTechniques 18:42044 (1995). In particular, RNA isolation can be performed using purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer's instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Other commercially available RNA isolation kits include MasterPure™ Complete DNA and RNA Purification Kit (EPICENTRE®, Madison, Wis.), and Paraffin Block RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from tumor can be isolated, for example, by cesium chloride density gradient centrifugation.

As RNA cannot serve as a template for PCR, the first step in gene expression profiling by RT-PCR is the reverse transcription of the RNA template into cDNA, followed by its exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avilo myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MMLV-RT). The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the circumstances and the goal of expression profiling. For example, extracted RNA can be reverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, Calif., USA), following the manufacturer's instructions. The derived cDNA can then be used as a template in the subsequent PCR reaction.

Although the PCR step can use a variety of thermostable DNA-dependent DNA polymerases, it typically employs the Taq DNA polymerase, which has a 5′-3′ nuclease activity but lacks a 3′-5′ proofreading endonuclease activity. Thus, TaqMan® PCR typically utilizes the 5′-nuclease activity of Taq or Tth polymerase to hydrolyze a hybridization probe bound to its target amplicon, but any enzyme with equivalent 5′ nuclease activity can be used. Two oligonucleotide primers are used to generate an amplicon typical of a PCR reaction. A third oligonucleotide, or probe, is designed to detect nucleotide sequence located between the two PCR primers. The probe is non-extendible by Taq DNA polymerase enzyme, and is labeled with a reporter fluorescent dye and a quencher fluorescent dye. Any laser-induced emission from the reporter dye is quenched by the quenching dye when the two dyes are located close together as they are on the probe. During the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resultant probe fragments disassociate in solution, and signal from the released reporter dye is free from the quenching effect of the second fluorophore. One molecule of reporter dye is liberated for each new molecule synthesized, and detection of the unquenched reporter dye provides the basis for quantitative interpretation of the data.

TaqMan® RT-PCR can be performed using commercially available equipment, such as, for example, ABI PRISM 7700™ Sequence Detection System™ (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), or Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In a preferred embodiment, the 5′ nuclease procedure is run on a real-time quantitative PCR device such as the ABI PRISM 7700™ Sequence Detection System™. The system consists of a thermocycler, laser, charge-coupled device (CCD), camera and computer. The system amplifies samples in a 96-well format on a thermocycler. During amplification, laser-induced fluorescent signal is collected in real-time through fiber optics cables for all 96 wells, and detected at the CCD. The system includes software for running the instrument and for analyzing the data.

5′-Nuclease assay data are initially expressed as Ct, or the threshold cycle. As discussed above, fluorescence values are recorded during every cycle and represent the amount of product amplified to that point in the amplification reaction. The point when the fluorescent signal is first recorded as statistically significant is the threshold cycle (C_(t)).

To minimize errors and the effect of sample-to-sample variation, RT-PCR is usually performed using an internal standard. The ideal internal standard is expressed at a constant level among different tissues, and is unaffected by the experimental treatment. RNAs most frequently used to normalize patterns of gene expression are mRNAs for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and β-actin.

A more recent variation of the RT-PCR technique is the real time quantitative PCR, which measures PCR product accumulation through a dual-labeled fluorigenic probe (i.e., TaqMan® probe). Real time PCR is compatible both with quantitative competitive PCR, where internal competitor for each target sequence is used for normalization, and with quantitative comparative PCR using a normalization gene contained within the sample, or a housekeeping gene for RT-PCR. For further details see, e.g. Held et al., Genome Research 6:986-994 (1996).

According to one aspect of the present invention, PCR primers and probes are designed based upon intron sequences present in the gene to be amplified. In this embodiment, the first step in the primer/probe design is the delineation of intron sequences within the genes. This can be done by publicly available software, such as the DNA BLAT software developed by Kent, W. J., Genome Res. 12(4):656-64 (2002), or by the BLAST software including its variations. Subsequent steps follow well established methods of PCR primer and probe design.

In order to avoid non-specific signals, it is important to mask repetitive sequences within the introns when designing the primers and probes. This can be easily accomplished by using the Repeat Masker program available on-line through the Baylor College of Medicine, which screens DNA sequences against a library of repetitive elements and returns a query sequence in which the repetitive elements are masked. The masked intron sequences can then be used to design primer and probe sequences using any commercially or otherwise publicly available primer/probe design packages, such as Primer Express (Applied Biosystems); MGB assay-by-design (Applied Biosystems); Primer3 (Steve Rozen and Helen J. Skaletsky (2000) Primer3 on the WWW for general users and for biologist programmers. In: Krawetz S, Misener S (eds) Bioinformatics Methods and Protocols: Methods in Molecular Biology. Humana Press, Totowa, N.J., pp 365-386)

The most important factors considered in PCR primer design include primer length, melting temperature (Tm), and G/C content, specificity, complementary primer sequences, and 3′-end sequence. In general, optimal PCR primers are generally 17-30 bases in length, and contain about 20-80%, such as, for example, about 50-60% G+C bases. Tm's between 50 and 80° C., e.g. about 50 to 70° C. are typically preferred.

For further guidelines for PCR primer and probe design see, e.g. Dieffenbach, C. W. et al., “General Concepts for PCR Primer Design” in: PCR Primer, A Laboratory Manual, Cold Spring Harbor Laboratory Press, New York, 1995, pp. 133-155; Innis and Gelfand, “Optimization of PCRs” in: PCR Protocols, A Guide to Methods and Applications, CRC Press, London, 1994, pp. 5-11; and Plasterer, T. N. Primerselect: Primer and probe design. Methods Mol. Biol. 70:520-527 (1997), the entire disclosures of which are hereby expressly incorporated by reference.

3. Microarrays

Differential gene expression can also be identified, or confirmed using the microarray technique. Thus, the expression profile of breast cancer-associated genes can be measured in either fresh or paraffin-embedded tumor tissue, using microarray technology. In this method, polynucleotide sequences of interest (including cDNAs and oligonucleotides) are plated, or arrayed, on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. Just as in the RT-PCR method, the source of mRNA typically is total RNA isolated from human tumors or tumor cell lines, and corresponding normal tissues or cell lines. Thus RNA can be isolated from a variety of primary tumors or tumor cell lines. If the source of mRNA is a primary tumor, mRNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples, which are routinely prepared and preserved in everyday clinical practice.

In a specific embodiment of the microarray technique, PCR amplified inserts of cDNA clones are applied to a substrate in a dense array. Preferably at least 10,000 nucleotide sequences are applied to the substrate. The microarrayed genes, immobilized on the microchip at 10,000 elements each, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance. With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pairwise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. The miniaturized scale of the hybridization affords a convenient and rapid evaluation of the expression pattern for large numbers of genes. Such methods have been shown to have the sensitivity required to detect rare transcripts, which are expressed at a few copies per cell, and to reproducibly detect at least approximately two-fold differences in the expression levels (Schena et al., Proc. Natl. Acad. Sci. USA 93(2):106-149 (1996)). Microarray analysis can be performed by commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix GenChip technology, or Incyte's microarray technology.

The development of microarray methods for large-scale analysis of gene expression makes it possible to search systematically for molecular markers of cancer classification and outcome prediction in a variety of tumor types.

4. Serial Analysis of Gene Expression (SAGE)

Serial analysis of gene expression (SAGE) is a method that allows the simultaneous and quantitative analysis of a large number of gene transcripts, without the need of providing an individual hybridization probe for each transcript. First, a short sequence tag (about 10-14 bp) is generated that contains sufficient information to uniquely identify a transcript, provided that the tag is obtained from a unique position within each transcript. Then, many transcripts are linked together to form long serial molecules, that can be sequenced, revealing the identity of the multiple tags simultaneously. The expression pattern of any population of transcripts can be quantitatively evaluated by determining the abundance of individual tags, and identifying the gene corresponding to each tag. For more details see, e.g. Velculescu et al., Science 270:484-487 (1995); and Velculescu et al., Cell 88:243-51 (1997).

5. MassARRAY Technology

The MassARRAY (Sequenom, San Diego, Calif.) technology is an automated, high-throughput method of gene expression analysis using mass spectrometry (MS) for detection. According to this method, following the isolation of RNA, reverse transcription and PCR amplification, the cDNAs are subjected to primer extension. The cDNA-derived primer extension products are purified, and dipensed on a chip array that is pre-loaded with the components needed for MALTI-TOF MS sample preparation. The various cDNAs present in the reaction are quantitated by analyzing the peak areas in the mass spectrum obtained.

6. Gene Expression Analysis by Massively Parallel Signature Sequencing (MPSS

This method, described by Brenner et al., Nature Biotechnology 18:630-634 (2000), is a sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 μm diameter microbeads. First, a microbead library of DNA templates is constructed by in vitro cloning. This is followed by the assembly of a planar array of the template-containing microbeads in a flow cell at a high density (typically greater than 3×10⁶ microbeads/cm²). The free ends of the cloned templates on each microbead are analyzed simultaneously, using a fluorescence-based signature sequencing method that does not require DNA fragment separation. This method has been shown to simultaneously and accurately provide, in a single operation, hundreds of thousands of gene signature sequences from a yeast cDNA library.

7. Immunohistochemistry

Immunohistochemistry methods are also suitable for detecting the expression levels of the prognostic markers of the present invention. Thus, antibodies or antisera, preferably polyclonal antisera, and most preferably monoclonal antibodies specific for each marker are used to detect expression. The antibodies can be detected by direct labeling of the antibodies themselves, for example, with radioactive labels, fluorescent labels, hapten labels such as, biotin, or an enzyme such as horse radish peroxidase or alkaline phosphatase. Alternatively, unlabeled primary antibody is used in conjunction with a labeled secondary antibody, comprising antisera, polyclonal antisera or a monoclonal antibody specific for the primary antibody. Immunohistochemistry protocols and kits are well known in the art and are commercially available.

8. Proteomics

The term “proteome” is defined as the totality of the proteins present in a sample (e.g. tissue, organism, or cell culture) at a certain point of time. Proteomics includes, among other things, study of the global changes of protein expression in a sample (also referred to as “expression proteomics”). Proteomics typically includes the following steps: (1) separation of individual proteins in a sample by 2-D gel electrophoresis (2-D PAGE); (2) identification of the individual proteins recovered from the gel, e.g. my mass spectrometry or N-terminal sequencing, and (3) analysis of the data using bioinformatics. Proteomics methods are valuable supplements to other methods of gene expression profiling, and can be used, alone or in combination with other methods, to detect the products of the prognostic markers of the present invention.

9. Improved Method for Isolation of Nucleic Acid from Archived Tissue Specimens

In the first step of the method of the invention, total RNA is extracted from the source material of interest, including fixed, paraffin-embedded tissue specimens, and purified sufficiently to act as a substrate in an enzyme assay. While extration of total RNA can be performed by any method known in the art, in a particular embodiment, the invention relies on an improved method for the isolation of nucleic acid from archived, e.g. fixed, paraffin-embedded tissue specimens (FPET).

Measured levels of mRNA species are useful for defining the physiological or pathological status of cells and tissues. RT-PCR (which is discussed above) is one of the most sensitive, reproducible and quantitative methods for this “gene expression profiling”. Paraffin-embedded, formalin-fixed tissue is the most widely available material for such studies. Several laboratories have demonstrated that it is possible to successfully use fixed-paraffin-embedded tissue (FPET) as a source of RNA for RT-PCR (Stanta et al., Biotechniques 11:304-308 (1991); Stanta et al., Methods Mol. Biol. 86:23-26 (1998); Jackson et al., Lancet 1:1391 (1989); Jackson et al., J. Clin. Pathol. 43:499-504 (1999); Finke et al., Biotechniques 14:448-453 (1993); Goldsworthy et al., Mol. Carcinog. 25:86-91 (1999); Stanta and Bonin, Biotechniques 24:271-276 (1998); Godfrey et al., J. Mol. Diagnostics 2:84 (2000); Specht et al., J. Mol. Med. 78:B27 (2000); Specht et al., Am. J. Pathol. 158:419-429 (2001)). This allows gene expression profiling to be carried out on the most commonly available source of human biopsy specimens, and therefore potentially to create new valuable diagnostic and therapeutic information.

The most widely used protocols utilize hazardous organic solvents, such as xylene, or octane (Finke et al., supra) to dewax the tissue in the paraffin blocks before nucleic acid (RNA and/or DNA) extraction. Obligatory organic solvent removal (e.g. with ethanol) and rehydration steps follow, which necessitate multiple manipulations, and addition of substantial total time to the protocol, which can take up to several days. Commercial kits and protocols for RNA extraction from FPET [MasterPure™ Complete DNA and RNA Purification Kit (EPICENTRE®, Madison, Wis.); Paraffin Block RNA Isolation Kit (Ambion, Inc.) and RNeasy™ Mini kit (Qiagen, Chatsworth, Calif.)] use xylene for deparaffinization, in procedures which typically require multiple centrifugations and ethanol buffer changes, and incubations following incubation with xylene.

The method that can be used in the present invention provides an improved nucleic acid extraction protocol that produces nucleic acid, in particular RNA, sufficiently intact for gene expression measurements. The key step in this improved nucleic acid extraction protocol is the performance of dewaxing without the use of any organic solvent, thereby eliminating the need for multiple manipulations associated with the removal of the organic solvent, and substantially reducing the total time to the protocol. According to the improved method, wax, e.g. paraffin is removed from wax-embedded tissue samples by incubation at 65-75° C. in a lysis buffer that solubilizes the tissue and hydrolyzes the protein, following by cooling to solidify the wax.

FIG. 2 shows a flow chart of the improved RNA extraction protocol used herein in comparison with a representative commercial method, using xylene to remove wax. The times required for individual steps in the processes and for the overall processes are shown in the chart. As shown, the commercial process requires approximately 50% more time than the improved process used in performing the methods of the invention.

The lysis buffer can be any buffer known for cell lysis. It is, however, preferred that oligo-dT-based methods of selectively purifying polyadenylated mRNA not be used to isolate RNA for the present invention, since the bulk of the mRNA molecules are expected to be fragmented and therefore will not have an intact polyadenylated tail, and will not be recovered or available for subsequent analytical assays. Otherwise, any number of standard nucleic acid purification schemes can be used. These include chaotrope and organic solvent extractions, extraction using glass beads or filters, salting out and precipitation based methods, or any of the purification methods known in the art to recover total RNA or total nucleic acids from a biological source.

Lysis buffers are commercially available, such as, for example, from Qiagen, Epicentre, or Ambion. A preferred group of lysis buffers typically contains urea, and Proteinase K or other protease. Proteinase K is very useful in the isolation of high quality, undamaged DNA or RNA, since most mammalian DNases and RNases are rapidly inactivated by this enzyme, especially in the presence of 0.5-1% sodium dodecyl sulfate (SDS). This is particularly important in the case of RNA, which is more susceptible to degradation than DNA. While DNases require metal ions for activity, and can therefore be easily inactivated by chelating agents, such as EDTA, there is no similar co-factor requirement for RNases.

Cooling and resultant solidification of the wax permits easy separation of the wax from the total nucleic acid, which can be conveniently precipitated, e.g. by isopropanol. Further processing depends on the intended purpose. If the proposed method of RNA analysis is subject to bias by contaminating DNA in an extract, the RNA extract can be further treated, e.g. by DNase, post purification to specifically remove DNA while preserving RNA. For example, if the goal is to isolate high quality RNA for subsequent RT-PCR amplification, nucleic acid precipitation is followed by the removal of DNA, usually by DNase treatment. However, DNA can be removed at various stages of nucleic acid isolation, by DNase or other techniques well known in the art.

While the advantages of the improved nucleic acid extraction discussed above are most apparent for the isolation of RNA from archived, paraffin embedded tissue samples, the wax removal step of the present invention, which does not involve the use of an organic solvent, can also be included in any conventional protocol for the extraction of total nucleic acid (RNA and DNA) or DNA only.

By using heat followed by cooling to remove paraffin, the improved process saves valuable processing time, and eliminates a series of manipulations, thereby potentially increasing the yield of nucleic acid.

10. 5′-Multiplexed Gene Specific Priming of Reverse Transcription

RT-PCR requires reverse transcription of the test RNA population as a first step. The most commonly used primer for reverse transcription is oligo-dT, which works well when RNA is intact. However, this primer will not be effective when RNA is highly fragmented as is the case in FPE tissues.

The present invention includes the use of gene specific primers, which are roughly 20 bases in length with a Tm optimum between about 58° C. and 60° C. These primers will also serve as the reverse primers that drive PCR DNA amplification.

An alternative approach is based on the use of random hexamers as primers for cDNA synthesis. However, we have experimentally demonstrated that the method of using a multiplicity of gene-specific primers is superior over the known approach using random hexamers.

11. Normalization Strategy

An important aspect of the present invention is to use the measured expression of certain genes by EGFR-expressing cancer tissue to provide information about the patient's likely response to treatment with an EGFR-inhibitor. For this purpose it is necessary to correct for (normalize away) both differences in the amount of RNA assayed and variability in the quality of the RNA used. Therefore, the assay typically measures and incorporates the expression of certain normalizing genes, including well known housekeeping genes, such as GAPDH and Cypl. Alternatively or in addition, normalization can be based on the mean or median signal (Ct in the case of RT-PCR) of all of the assayed genes or a large subset thereof (global normalization approach). On a gene-by-gene basis, measured normalized amount of a patient tumor mRNA is compared to the amount found in a reference set of cancer tissue of the same type (e.g. head and neck cancer, colon cancer, etc.). The number (N) of cancer tissues in this reference set should be sufficiently high to ensure that different reference sets (as a whole) behave essentially the same way. If this condition is met, the identity of the individual cancer tissues present in a particular set will have no significant impact on the relative amounts of the genes assayed. Usually, the cancer tissue reference set consists of at least about 30, preferably at least about 40 different FPE cancer tissue specimens. Unless noted otherwise, normalized expression levels for each mRNA/tested tumor/patient will be expressed as a percentage of the expression level measured in the reference set. More specifically, the reference set of a sufficiently high number (e.g. 40) of tumors yields a distribution of normalized levels of each mRNA species. The level measured in a particular tumor sample to be analyzed falls at some percentile within this range, which can be determined by methods well known in the art. Below, unless noted otherwise, reference to expression levels of a gene assume normalized expression relative to the reference set although this is not always explicitly stated.

12. EGFR Inhibitors

The epidermal growth factor receptor (EGFR) family (which includes EGFR, erb-B2, erb-B3, and erb-B4) is a family of growth factor receptors that are frequently activated in epithelial malignancies. Thus, the epidermal growth factor receptor (EGFR) is known to be active in several tumor types, including, for example, ovarian cancer, pancreatic cancer, non-small cell lung cancer, breast cancer, colon cancer and head and neck cancer. Several EGFR inhibitors, such as ZD1839 (also known as gefitinib or Iressa); and OSI774 (Erlotinib, Tarceva™), are promising drug candidates for the treatment of EGFR-expressing cancer.

Iressa, a small synthetic quinazoline, competitively inhibits the ATP binding site of EGFR, a growth-promoting receptor tyrosine kinase, and has been in Phase III clinical trials for the treatment of non-small-cell lung carcinoma. Another EGFR inhibitor, [agr]cyano-[bgr]methyl-N-[(trifluoromethoxy)phenyl]-propenamide (LFM-A12), has been shown to inhibit the proliferation and invasiveness of EGFR positive human breast cancer cells.

Cetuximab is a monoclonal antibody that blocks the EGFR and EGFR-dependent cell growth. It is currently being tested in phase III clinical trials.

Tarceva™ has shown promising indications of anti-cancer activity in patients with advanced ovarian cancer, and non-small cell lung and head and neck carcinomas.

The present invention provides valuable tools to predict whether an EGFR-positive tumor is likely to respond to treatment with an EGFR-inhibitor.

Recent publications further confirm the involvement of EGFR in gastrointestinal (e.g. colon) cancer, and associate its expression with poor survival. See, e.g. Khorana et al., Proc. Am. Soc. Clin. Oncol 22:317 (2003).

While the listed examples of EGFR inhibitors a small organic molecules, the findings of the present invention are equally applicable to other EGFR inhibitors, including, without limitation, anti-EGFR antibodies, antisense molecules, small peptides, etc.

Further details of the invention will be apparent from the following non-limiting Examples.

EXAMPLE 1

A Phase II Study of Gene Expression in Head and Neck Tumors

A gene expression study was designed and conducted with the primary goal to molecularly characterize gene expression in paraffin-embedded, fixed tissue samples of head and neck cancer patients who responded or did not respond to treatment with an EGFR inhibitor. The results are based on the use of five different EGFR inhibitor drugs.

Study Design

Molecular assays were performed on paraffin-embedded, formalin-fixed head and neck tumor tissues obtained from 14 individual patients diagnosed with head and neck cancer. Patients were included in the study only if histopathologic assessment, performed as described in the Materials and Methods section, indicated adequate amounts of tumor tissue.

Materials and Methods

Each representative tumor block was characterized by standard histopathology for diagnosis, semi-quantitative assessment of amount of tumor, and tumor grade. A total of 6 sections (10 microns in thickness each) were prepared and placed in two Costar Brand Microcentrifuge Tubes (Polypropylene, 1.7 mL tubes, clear; 3 sections in each tube). If the tumor constituted less than 30% of the total specimen area, the sample may have been crudely dissected by the pathologist, using gross microdissection, putting the tumor tissue directly into the Costar tube.

If more than one tumor block was obtained as part of the surgical procedure, all tumor blocks were subjected to the same characterization, as described above, and the block most representative of the pathology was used for analysis.

Gene Expression Analysis

mRNA was extracted and purified from fixed, paraffin-embedded tissue samples, and prepared for gene expression analysis as described above.

Molecular assays of quantitative gene expression were performed by RT-PCR, using the ABI PRISM 7900™ Sequence Detection System™ (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA). ABI PRISM 7900™ consists of a thermocycler, laser, charge-coupled device (CCD), camera and computer. The system amplifies samples in a 384-well format on a thermocycler. During amplification, laser-induced fluorescent signal is collected in real-time through fiber optics cables for all 384 wells, and detected at the CCD. The system includes software for running the instrument and for analyzing the data.

Analysis and Results

Tumor tissue was analyzed for 185 cancer-related genes and 7 reference genes. The threshold cycle (CT) values for each patient were normalized based on the mean of all genes for that particular patient. Clinical outcome data were available for all patients.

Outcomes were classified as either response or no response. The results were analyzed in two different ways using two different criteria for response: partial response, or clinical benefit. The latter criterion combines partial or complete response with stable disease (minimum 3 months). In this study, there were no complete responses, four cases of partial response and two cases of disease stabilization.

We evaluated the relationship between gene expression and partial response by logistic regression and have identified the following genes as significant (p<0.15), as indicated in the attached Table 1. The logistic model provides a means of predicting the probability (Pr) of a subject as being either a partial responder or not. The following equation defined the expression threshold for response. Pr(Response)=1/1+e ^(Intercep+Slope×Reference Normalized CT) and Pr(No Response)=1−Pr(Response)

In Table 1, the term “negative” indicates that greater expression of the gene decreased likelihood of response to treatment with EGFR inhibitor, and “positive” indicates that increased expression of the gene increased likelihood of response to EGFR inhibitor. Results from analysis of head and neck cancer patient data using clinical benefit criteria are shown in Table 2.

Overall increased expression of the following genes correlated with resistance of head and neck cancer to EGFR inhibitor treatment: A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CCNA2; CCNE1; CCNE2; CD105; CD44v3; CD44v6; CD68; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; PAI1; PDGFA; PGK1; PTPD1; RANBP2; SPRY2; TP53BP1; and VEGFC; and increased expression of the following genes correlated with response of head and neck cancer to EGFR inhibitor treatment: CD44s; CD82; CGA; CTSL; EGFRd27; IGFBP3; p27; P53; RB1; TIMP2; and YB-1.

EXAMPLE 2

A Phase II Study of Gene Expression in Colon Cancer

In a study analogous to the study of head and neck cancer patients described in Example 1, gene expression markers were sought that correlate with increased or decreased likelihood of colon cancer response to EGFR inhibitors. Sample preparation and handling and gene expression and data analysis were performed as in Example 1.

Twenty-three colon adenocarcinoma patients in all were studied, using a 192 gene assay. 188 of the 192 genes were expressed above the limit of detection. Both pathological and clinical responses were evaluated. Following treatment with EGFR inhibitor, three patients were determined to have had a partial response, five to have stable disease and fifteen to have progressive disease.

Table 3 shows the results obtained using the partial response criterion.

Results from analysis of colon cancer patient data using clinical benefit criteria are shown in Table 4.

Overall, increased expression of the following genes correlated with resistance of colon cancer to EGFR inhibitor treatment: CA9; CD134; CD44E; CD44v3; CD44v6; CDC25B; CGA; DR5; GRO1; KRT17; LAMC2; P14ARF; PDGFB; PLAUR; PPARG; RASSF1; RIZ1; Src; TFRC; and UPA, and increased expression of the following genes correlated with sensitivity of colon cancer to EGFR inhibitor treatment: CD44s; CD82; CGA; CTSL; EGFRd27; IGFBP3; p27; P53; RB1; TIMP2; and YB-1.

Finally, it is noteworthy that increased expression of the following genes correlated with resistance to EGFR inhibitor treatment in both head and neck and colon cancer: CD44v3; CD44v6; DR5; GRO1; KRT17; LAMC2.

In similar experiments, the elevated expression of LAMC2, B-Catenin, Bax, GRO1, Fas, or ITGA3 in EGFR-positive head and neck cancer was determined to be an indication that the patient is not likely to respond well to treatment with an EGFR inhibitor. On the other hand, elevated expression of YB-1, PTEN, CTSL, P53, STAT3, ITGB3, IGFBP3, RPLPO or p27 in EGFR-positive head and neck cancer was found to be an indication that the patient is likely to respond to EGFR inhibitor treatment.

In another set of similar experiments, elevated expression of the following genes in EGFR-expressing colon cancer correlated with positive response to treatment: BAK; BCL2; BRAF; BRK; CCND3; CD9; ER2; ERBB4; EREG; ERK1; FRP1. Elevated expression of the following genes in EGFR-expressing colon cancer correlated with resistance to treatment: APN; CA9; CCND1; CDC25B; CD134; LAMC2; PDGFB; CD44v6; CYP1; DR5; GAPDH; IGFBP2; PLAUR; RASSF1; UPA.

All references cited throughout the specification are hereby expressly incorporated by reference.

Although the present invention is illustrated with reference to certain embodiments, it is not so limited. Modifications and variations are possible without diverting from the spirit of the invention. All such modifications and variations, which will be apparent to those skilled in the art, are specifically within the scope of the present invention. While the specific examples disclosed herein concern head and neck cancer and colon cancer, the methods of the present invention are generally applicable and can be extended to all EGFR-expressing cancers, and such general methods are specifically intended to be within the scope herein. TABLE 1 Partial Response Genes for Head and Neck Study Likelihood Logistic Discriminat Function Ratio Test Gene Name Response Intercept Slope R2 P Value cMet Negative 26.5168713 4.57143179 0.6662 0.0011 LAMC2 Negative 5.29706425 1.28137295 0.6155 0.0017 ITGA3 Negative 22.6008544 3.17707499 0.5063 0.0044 CD44v6 Negative 6.92255059 4.3069909 0.492 0.005 B-Catenin Negative 7.85913706 2.52965454 0.4805 0.0055 PDGFA Negative 6.0016358 1.10386463 0.4318 0.0085 GRO1 Negative 8.37646635 1.74815793 0.4146 0.0099 ERK1 Negative 6.14712633 1.64819007 0.4024 0.0111 CD44v3 Negative 5.95094528 3.36594473 0.3451 0.0186 Bax Negative 5.34006632 1.19383253 0.3361 0.0202 CGA Positive −78.121148 −10.503757 0.3266 0.0221 fas Negative 7.27491015 1.38464586 0.3251 0.0224 IGFBP3 Positive −2.1529531 −2.7937517 0.3097 0.0258 MTA1 Negative 6.07167277 1.23786874 0.3072 0.0264 YB-1 Positive 1.73598983 −4.0859174 0.2814 0.0336 DR5 Negative 9.0550349 1.46349944 0.2703 0.0373 APC Negative 5.775003 1.88324269 0.2512 0.0447 ERBB4 Negative 11.9466285 1.58606697 0.2357 0.0518 CD68 Negative 3.60605487 1.0645631 0.2319 0.0537 cripto Negative 19.5004373 2.64909385 0.2251 0.0574 P53 Positive −4.1976158 −1.5541169 0.2208 0.0598 VEGFC Negative 6.33634489 0.90613473 0.2208 0.0598 A-Catenin Negative 4.41215235 1.7591194 0.2199 0.0603 COX2 Negative 8.00968996 1.27597736 0.202 0.0718 CD82 Positive −1.8999985 −1.171157 0.1946 0.0772 PAI1 Negative 2.94777884 0.97480364 0.1944 0.0774 AKT2 Negative 2.45598587 1.64608189 0.1889 0.0817 HER2 Negative 4.25059223 0.97748483 0.1845 0.0853 DIABLO Negative 17.035069 2.93939741 0.1809 0.0884 p27 Positive −1.9798519 −1.9041142 0.1792 0.09 RANBP2 Negative 2.85994976 0.41878666 0.1757 0.0931 EIF4E Negative 2.91202768 0.56099402 0.1722 0.0965 EDN1 endothelin Negative 6.06858911 0.87185553 0.1688 0.0998 IGF1R Negative 6.14387144 1.68865744 0.1674 0.1012 AKT1 Negative 5.02676228 1.50585593 0.1659 0.1028 CCNA2 Negative 3.95684559 0.63089954 0.184 0.1033 HB-EGF Negative 5.1019713 0.70368632 0.1627 0.1061 TIMP2 Positive 2.58975885 −1.0832648 0.1625 0.1064 EGFRd27 Positive −38.789016 −5.2513587 0.1607 0.1083 Chk2 Negative 6.8797175 1.21671205 0.1581 0.1112 IRS1 Negative 12.0545078 1.59632708 0.1578 0.1115 FRP1 Negative 3.38233862 0.49053452 0.1569 0.1126 CCNE2 Negative 5.78828731 1.11609099 0.1566 0.1129 SPRY2 Negative 4.68447069 0.86747803 0.1552 0.1145 KRT17 Negative 0.34280253 0.412313 0.151 0.1195 DPYD Negative 2.78071456 0.78918833 0.1504 0.1202 CD105 Negative 3.13613733 0.51406689 0.1391 0.1351 TP53BP1 Negative 3.18676588 0.58622276 0.1361 0.1395 PTPD1 Negative 5.85217342 1.08545385 0.1357 0.1401 CTSL Positive −2.2283797 −1.4833372 0.1354 0.1405

TABLE 2 Clinical Benefit Genes for Head and Neck Study Logistic Discriminat Likelihood Function Ratio Test Gene Name Response Intercept Slope R² P Value cMet.2 Negative 23.583252 4.4082875 0.6444 0.0007 GRO1.2 Negative 10.10717 2.46904056 0.5388 0.0019 A-Catenin.2 Negative 5.13298651 2.60834812 0.3628 0.0107 AKT1.3 Negative 7.7652606 2.83068092 0.3044 0.0194 DCR3.3 Negative 10.2957141 1.85012996 0.293 0.0219 B-Catenin.3 Negative 4.21267279 1.5417788 0.2791 0.0252 EDN1 endothelin.1 Negative 6.83022814 1.14550062 0.2758 0.0261 CCNE1.1 Negative 7.43731399 1.21270723 0.2661 0.0289 LAMC2.2 Negative 1.79659862 0.56623898 0.2498 0.0342 CD44v6.1 Negative 2.55050577 1.87838162 0.2071 0.0539 DIABLO.1 Negative 16.5051841 2.99910512 0.2066 0.0542 CD44v3.2 Negative 3.02492619 2.05469571 0.2002 0.058 NMYC.2 Negative 23.2010327 3.20767305 0.1955 0.061 CD82.3 Positive 2.7521937 −1.1692268 0.188 0.0662 RANBP2.3 Negative 2.02076788 0.42173233 0.1807 0.0718 RB1.1 Positive −5.7352964 −1.7540651 0.1761 0.0754 HER2.3 Negative 3.87564158 1.11486016 0.1732 0.0779 MTA1.1 Negative 3.9020256 0.92255645 0.1628 0.0874 CGA.3 Positive −41.909839 −5.5686182 0.1619 0.0883 CEACAM6.1 Negative 1.66596967 0.59307792 0.1602 0.0899 PTPD1.2 Negative 5.51242763 1.18616068 0.1601 0.0901 ERK1.3 Negative 2.4144706 0.72072834 0.154 0.0964 Bax.1 Negative 2.91338256 0.76334619 0.152 0.0987 STMY3.3 Positive −0.9946728 −0.6053981 0.1483 0.1028 COX2.1 Negative 5.79279616 1.0312018 0.1478 0.1034 EIF4E.1 Negative 2.08005397 0.55985052 0.1468 0.1045 YB-1.2 Positive 0.45158771 −2.2935538 0.1426 0.1096 fas.1 Negative 4.05538424 0.8686042 0.1397 0.1134 PDGFA.3 Negative 2.43388275 0.53168307 0.1371 0.1168 FRP1.3 Negative 2.17320245 0.41529609 0.137 0.1169 PGK1.1 Negative 1.86416703 1.92395917 0.1338 0.1212 AKT2.3 Negative 1.45131206 1.43341036 0.1281 0.1294 BTC.3 Negative 12.1153734 1.67411928 0.1281 0.1294 APC.4 Negative 2.50791938 0.92506412 0.128 0.1296 CCNE2.2 Negative 3.98727145 0.89372321 0.1267 0.1315 OPN, osteopontin.3 Positive −0.522697 −0.5069258 0.1225 0.1382 ITGA3.2 Negative 2.23381763 0.3800099 0.1203 0.1417 KRT17.2 Negative −0.4861169 0.43917211 0.1184 0.1449 CD44s.1 Positive −0.9768133 −0.8896223 0.118 0.1456 EGFR.2 Negative 0.43258354 0.46719029 0.1162 0.1487

TABLE 3 Partial Response Genes for Colon Study Likeli- hood Logistic Discriminat Ratio Function Test Gene Name Response Intercept Slope R² P Value Bclx_2 Positive 2.04896151 −2.1025144 0.172 0.0801 BRAF_2 Positive −2.5305788 −3.0987684 0.2532 0.0337 BRK_2 Positive −2.6096501 −1.577388 0.2998 0.0209 CA9_3 Negative 2.65287578 0.83720397 0.2758 0.0267 Cad17_1 Positive −0.0419396 −1.8773242 0.2096 0.0533 CCND3_1 Positive −1.014844 −5.1111617 0.348 0.0128 CCNE1_1 Positive −6.5821701 −0.8939912 0.1914 0.0648 CCNE2_2 Positive 26.1675642 −1.0709109 0.1707 0.0812 CD105_1 Positive 5.85359096 −1.2349006 0.1302 0.1278 CD134_2 Negative −5.9286576 1.51119518 0.1212 0.1418 CD44v3_2 Negative −1.8184898 1.12771829 0.2064 0.0552 CDC25B_1 Negative 10.4351019 1.59196005 0.2455 0.0365 DR5_2 Negative −1.7399226 1.60177588 0.1759 0.0767 ErbB3_1 Positive 3.65681435 −0.760436 0.1222 0.1401 EREG_1 Positive −2.3409861 −1.1217612 0.2542 0.0333 GPC3_1 Positive 4.03889935 −1.9097648 0.3752 0.0097 GRO1.2 Negative 2.77545378 0.74734483 0.124 0.1359 GUS_1 Positive 8.29578416 −1.9015759 0.2105 0.0529 HGF_4 Positive 5.10609383 −1.1947949 0.2361 0.0403 ID1_1 Positive 10.6703203 −1.654146 0.216 0.0498 ITGB3_1 Positive 0.79232612 −0.827508 0.3321 0.015 KRT17_2 Negative 5.93738146 0.93514633 0.2133 0.0513 LAMC2_2 Negative −0.3325052 1.41542034 0.2475 0.0357 P14ARF_1 Negative 4.36456658 4.10859002 0.2946 0.022 PDGFB_3 Negative −4.7055966 1.96517114 0.3299 0.0154 PLAUR_3 Negative 7.51817646 0.6862142 0.1534 0.0983 PTPD1_2 Positive −11.659761 −1.2559081 0.1247 0.1362 RASSF1_3 Negative 6.60631474 0.9862129 0.1708 0.0811 RIZ1_2 Negative 2.83817546 0.86281199 0.1255 0.1349 Src_2 Negative 4.91364145 1.96089745 0.1324 0.1247 TFRC_3 Negative −4.0754666 3.03617052 0.19 0.0658 TITF1_1 Positive −1.8849815 −2.1890987 0.1349 0.1211 upa_3 Negative 4.1059421 1.14053848 0.1491 0.1032 XIAP_1 Positive −16.296951 −2.9502191 0.2661 0.0295

TABLE 4 Clinical Benefit Genes for Colon Study Logistic Discriminat Likelihood Function Ratio Test Gene Name Response Intercept Slope R² P Value  Bak Positive −1.347937 −0.993212 0.1189 0.0602 BRK Positive −3.237705 −1.1479379 0.2567 0.0057 CD134 Negative 9.9358537 1.68440149 0.1927 0.0167 CD44E Negative 3.188991 0.59091622 0.0958 0.0916 CD44v6 Negative 5.7352464 1.77571293 0.2685 0.0047 CDC25B Negative 2.0664209 0.67140598 0.0783 0.1272 CGA Negative 2.7903424 0.43834476 0.1035 0.0794 COX2 Positive −1.262804 −0.4741852 0.0733 0.1398 DIABLO Positive −2.514199 −1.0753148 0.1028 0.0805 FRP1 Positive −0.401936 −0.3555899 0.0937 0.0952 GPC3 Positive −7.875276 −1.7437079 0.3085 0.0025 HER2 Positive 0.1228609 −0.5549133 0.073 0.1408 ITGB3 Positive −1.593092 −0.5249778 0.1352 0.045 PPARG Negative 8.6479233 1.36115361 0.1049 0.0774 PTPD1 Positive −3.203607 −1.2049773 0.1356 0.0447 RPLPO Positive 3.5110353 −1.030518 0.0752 0.135 STK15 Positive −0.664989 −0.5936475 0.0873 0.1072 SURV Positive −1.409619 −0.6214924 0.074 0.1381 TERC Positive 1.7755749 −0.5180083 0.1073 0.0742 TGFBR2 Positive 1.5172396 −0.9288498 0.0934 0.0957

TABLES 5A-5B Seq. Gene Accessin Sequence ID A-Catenin NM_00190 CGTTCCGATCCTCTATACTGCATCCCAGGCATGCCTACAGCACCCTGATGTCGCAGCCTATAAGGCCAACAGGGACC 1 T AKT1 NM_00516 CGCTTCTATGGCGCTGAGATTGTGTCAGCCCTGGACTACCTGCACTCGGAGAAGAACGTGGTGTACCGGGA 2 AKT2 NM_00162 TGCTGCCACGCTTCAAACGTCAGGTCACGTCCGAGGTCGACACAAGGTACTTCGATGATGAATTTACCGCC 3 APC NM_00003 GGACAGCAGGAATGTGTTTCTCCATACAGGTCACGGGGAGCCAATGGTTCAGAAACAAATCGAGTGGGT 4 B-Catenin NM_00190 GGCTCTTGTGCGTACTGTCCTTCGGGCTGGTGACAGGGAAGACATCACTGAGCCTGCCATCTGTGCTCTTCGTCATC 5 TGA Bak NM_00118 CCATTCCCACCATTCTACCTGAGGCCAGGACGTCTGGGGTGTGGGGATTGGTGGGTCTATGTTCCC 6 Bax NM_00432 CCGCCGTGGACACAGACTCCCCCCGAGAGGTCTTTTTCCGAGTGGCAGCTGACATGTTTTCTGACGGCAA 7 Bclx NM_00119 CTTTTGTGGAACTCTATGGGACAATGCAGCAGCCGAGAGCCGAAGGGCCAGGAACGCTTCAACCGCTG 8 BRAF NM_00433 CCTTCCGACCAGCAGATGAAGATCATCGAAATCAATTTGGGCAACGAGACCGATCCTCATCAGCTCCCAATGTGCAT 9 ATAAA BRK NM_00597 GTGCAGGAAAGGTTCACAAATGTGGAGTGTCTGCGTCCAATACACGCGTGTGCTCCTCTCCTTACTCCATCGTGTGT 10 GC BTC NM_00172 AGGGAGATGCCGCTTCGTGGTGGCCGAGCAGACGCCCTCCTGTGTCTGTGATGAAGGCTACATTGGAGCAAGGTGTG 11 AGAG CA9 NM_00121 ATCCTAGCCCTGGTTTTTGGCCTCCTTTTTGCTGTCACCAGCGTCGCGTTCCTTGTGCAGATGAGAAGGCAG 12 Cad17 NM_00406 GAAGGCCAAGAACCGAGTCAATTATATTCCAGTTTAAGGCCAATCCTCCTGCTGTGACTTTTGAACTAACTGGGGA 13 CCNA2 NM_00123 CCATACCTCAAGTATTTGCCATCAGTTATTGCTGGAGCTGCCTTTCATTTAGCACTCTACACAGTCACGGGACAAAG 14 CT CCND3 NM_00176 CCTCTGTGCTACAGATTATACCTTTGCCATGTACCCGCCATCCATGATCGCCACGGGCAGCATTGGGGCTGCAGTG 15 CCNE1 NM_00123 AAAGAAGATGATGACCGGGTTTACCCAAACTCAACGTGCAAGCCTCGGATTATTGCACCATCCAGAGGCTC 16 CCNE2 NM_05774 ATGCTGTGGCTCCTTCCTAACTGGGGCTTTCTTGACATGTAGGTTGCTTGGTAATAACCGTATATCACAATTTGGGT 17 CD105 NM_00011 GCAGGTGTCAGCAAGTATGATCAGCAATGAGGCGGTGGTCAATATCCTGTCGAGCTCATCACCACAGCGAAAAA 18 CD134 NM_00332 GCCCAGTGCGGAGAACAGGTCCAGCTTGATTCTCGTCTCTGCACTTAAGCTGTTCTCCAGGTGCGTGTGATT 19 CD44E X55150 ATCACCGACAGCACAGACAGAATCCCTGCTACCAATATGGACTCCAGTCATAGTACAACGCTTCAGCCTACTGCAAA 20 TCCAAACACAGGT CD44s M59040 GACGAAGACAGTCCCTGGATCACCGACAGCACAGACAGAATCCCTGCTACCAGAGACCAAGACACATTCCACCCCAG 21 T CD44v3 AJ251595\ CACACAAAACAGAACCAGGACTGGACCCAGTGGAACCCAAGCCATTCAAATCCGGAAGTGCTACTTCAG 22 CD44v6 AJ251595\ CTCATACCAGCCATCCAATGCAAGGAAGGACAACACCAAGCCCAGAGGACAGTTCCTGGACTGATTTCTTCAACCCA 23 A CD68 NM_00125 TGGTTCCCAGCCCTGTGTCCACCTCCAAGCCCAGATTCAGATTCGAGTCATGTACACAACCCAGGGTGGAGGAG 24 CD82 NM_00223 GTGCAGGCTCAGGTGAAGTGCTGCGGCTGGGTCAGCTTCTACAACTGGACAGACAACGCTGAGCTCATGAATCGCCC 25 TGAGGTC CD9 NM_00176 GGGCGTGGAACAGTTTATCTCAGACATCTGCCCCAAGAAGGACGTACTCGAAACCTTCACCGTG 26 CDC25B NM_02187 AAACGAGCAGTTTGCCATCAGACGCTTCCAGTCTATGCCGGTGAGGCTGCTGGGCCACAGCCCCGTGCTTCGGAACA 27 TCACCAAC CEACAM6 NM_00248 CACAGCCTCACTTCTAACCTTCTGGAACCCACCCACCACTGCCAAGCTCACTATTGAATCCACGCCATTCAA 28 CGA NM_00127 CTGAAGGAGCTCCAAGACCTCGCTCTCCAAGGCGCCAAGGAGAGGGCACATCAGCAGAAGAAACACAGCGGTTTTG 29 Chk2 NM_00719 ATGTGGAACCCCCACCTACTTGTGCGCCTGAAGTTCTTGTTTCTGTTGGGACTGCTGGGTATAACCGTGCTGTGGAC 30 TG cMet NM_00024 GACATTTCCAGTCCTGCAGTCAATGCCTCTCTGCCCCACCCTTTGTTCAGTGTGGCTGGTGCCACGACAAATGTGTG 31 CGATCGGAG COX2 NM_00096 TCTGCAGAGTTGGAAGCACTCTATGGTGACATCGATGCTGTGGAGCTGTATCCTGCCCTTCTGGTAGAAAAGCCTCG 32 GC cripto NM_00321 GGGTCTGTGCCCCATGACACCTGGCTGCCCAAGAAGTGTTCCCTGTGTAAATGCTGGCACGGTCA 33 CTSL NM_00191 GGGAGGCTTATCTCACTGAGTGAGCAGAATCTGGTAGACTGCTCTGGGCCTCAAGGCAATGAAGGCTGCAATGG 34 DCR3 NM_01643 GACCAAGGTCCTGGAATGTCTGCAGCAGAAGGTGAATGGCATCCTGGAGAGCCCTACGGGTACAGGGAAGAC 35 DIABLO NM_01988 CACAATGGCGGCTCTGAAGAGTTGGCTGTCGCGCAGCGTAACTTCATTCTTCAGGTACAGACAGTGTTTGTGT 36 DPYD NM_00011 AGGACGCAAGGAGGGTTTGTCACTGGCAGACTCGAGACTGTAGGCACTGCCATGGCCCCTGTGCTCAGTAAGGACTC 37 GGCGGACATC DR5 NM_00384 CTCTGAGACAGTGCTTCGATGACTTTGCAGACTTGGTGCCCTTTGACTCCTGGGAGCCGCTCATGAGGAAGTTGGGC 38 CTCATGG EDN1 end NM_00195 TGCCACCTGGACATCATTTGGGTCAACACTCCCGAGCACGTTGTTCCGTATGGACTTGGAAGCCCTAGGTCCA 39 EGFR NM_00522 TGTCGATGGACTTCCAGAACCACCTGGGCAGCTGCCAAAAGTGTGATCCAAGCTGTCCCAAT 40 EGFRd27 EGFRd27 GAGTCGGGCTCTGGAGGAAAAGAAAGGTAATTATGTGGTGACAGATCACGGCTCGTGCGTCCGAGCCTGTGG 41 EIF4E NM_00196 GATCTAAGATGGCGACTGTCGAACCGGAAACCACCCCTACTCCTAATCCCCCGACTACAGAAGAGGAGAAAACGGAA 42 TCTAA ErbB3 NM_00198 CGGTTATGTCATGCCAGATACACACCTCAAAGGTACTCCCTCCTCCCGGGAAGGCACCCTTTCTTCAGTGGGTCTCA 43 GTTC ERBB4 NM_00523 TGGCTCTTAATCAGTTTCGTTACCTGCCTCTGGAGAATTTACGCATTATTCGTGGGACAAAACTTTATGAGGATCGA 44 TATGCCTTG EREG NM_00143 ATAACAAAGTGTAGCTCTGACATGAATGGCTATTGTTTGCATGGACAGTGCATCTATCTGGTGGACATGAGTCAAAA 45 CTACTGCAGGT ERK1 Z11696 ACGGATCACAGTGGAGGAAGCGCTGGCTCACCCCTACCTGGAGCAGTACTATGACCCGACGGATGAG 46 fas NM_00004 GGATTGCTCAACAACCATGCTGGGCATCTGGACCCTCCTACCTCTGGTTCTTACGTCTGTTGCTAGATTATCGTCCA 47 AAAGTGTTAATGCC FRP1 NM_00301 TTGGTACCTGTGGGTTAGCATCAAGTTCTCCCCAGGGTAGAATTCAATCAGAGCTCCAGTTTGCATTTGGATGTG 48 GPC3 NM_00448 TGATGCGCCTGGAAACAGTCAGCAGGCAACTCCGAAGGACAACGAGATAAGCACCTTTCACAACCTCG 49 GRO1 NM_00151 CGAAAAGATGCTGAACAGTGACAAATCCAACTGACCAGAAGGGAGGAGGAAGCTCACTGGTGGCTGTTCCTGA 50 GUS NM_00018 CCCACTCAGTAGCCAAGTCACAATGTTTGGAAAACAGCCCGTTTACTTGAGCAAGACTGATACCACCTGCGTG 51 HB-EGF NM_00194 GACTCCTTCGTCCCCAGTTGCCGTCTAGGATTGGGCCTCGCATAATTGCTTTGCCAAAATACGAGAGCCTTCAAGTG 52 CCA HER2 NM_00444 CGGTGTGAGAAGTGCAGCAAGCCCTGTGCCCGAGTGTGCTATGGTCTGGGCATGGAGCACTTGCGAGAGG 53 HGF M29145 CCGAAATCCAGATGATGATGCTCATGGACCCTGGTGCTACACGGGAAATCCACTCATTCCTTGGG 54 ID1 NM_00216 AGAACCGCAAGGTGAGCAAGGTGGAGATTCTCCAGCACGTCATCGACTACATCAGGGACCTTCAGTTGGA 55 IGF1R NM_00087 GCATGGTAGCCGAAGATTTCACAGTCAAAATCGGAGATTTTGGTATGACGCGAGATATCTATGAGACAGACTATTAC 56 CGGAAA IGFBP3 NM_00059 ACGCACCGGGTGTCTGATCCCAAGTTCCACCCCCTCCATTCAAAGATAATCATCATCAAGAAAGGGCA 57 IRS1 NM_00554 CCACAGCTCACCTTCTGTCAGGTGTCGATCCCAGCTCCAGCCAGCTCCCAGAGAGGAAGAGACTGGCACTGAGG 58 ITGA3 NM_00220 CCATGATCCTCACTCTGCTGGTGGACTATACACTCCAGACCTCGCTTAGCATGGTAAATCACCGGCTACAAAGCTTC 59 ITGB3 NM_00021 ACCGGGAGCCCTACATGACCGAAAATACCTGCAACCGTTACTGCCGTGACGAGATTGAGTCAGTGAAAGAGCTTAAG 60 G KRT17 NM_00042 CGAGGATTGGTTCTTCAGCAAGACAGAGGAACTGAACCGCGAGGTGGCCACCAACAGTGAGCTGGTGGAGAGT 61 LAMC2 NM_00556 ACTCAAGCGGAAATTGAAGCAGATAGGTCTTATCAGCACAGTCTCCGCCTCCTGGATTCAGTGTCTCGGCTTCAGGG 62 AGT MTA1 NM_00468 CCGCCCTCACCTGAAGAGAAACGCGCTCCTTGGCGGACACTGGGGGAGGAGAGGAAGAAGCGCGGCTAACTTATTCC 63 NMYC NM_00537 TGAGCGTCGCAGAAACCACAACATCCTGGAGCGCCAGCGCCGCAACGACCTTCGGTCCAGCTTTCTCACGCTCAGGG 64 A p14ARF NM_00007 GCGGAAGGTCCCTCAGACATCCCCGATTGAAAGAACCAGAGAGGCTCTGAGAAACCTCGGGAAACTTAGA 65 p27 NM_00406 CGGTGGACCACGAAGAGTTAACCCGGGACTTGGAGAAGCACTGCAGAGACATGGAAGAGGCGAGCC 66 P53 NM_00054 CTTTGAACCCTTGCTTGCAATAGGTGTGCGTCAGAAGCACCCAGGACTTCCATTTGCTTTGTCCCGGG 67 PAI1 NM_00060 CCGCAACGTGGTTTTCTCACCCTATGGGGTGGCCTCGGTGTTGGCCATGCTCCAGCTGACAACAGGAGGAGAAACGC 68 AGCA PDGFA NM_00260 TTGTTGGTGTGCCCTGGTGCCGTGGTGGCGGTCACTCCCTCTGCTGCCAGTGTTTGGACAGAACCCA 69 PDGFB NM_00260 ACTGAAGGAGACCCTTGGAGCCTAGGGGCATCGGCAGGAGAGTGTGTGGGCAGGGTTATTTA 70 PGK1 NM_00029 AGAGCCAGTTGCTGTAGAACTCAAATCTCTGGTGGGCAAGGATGTTCTGTTCTTGAAGGACTGTGTAGGCCCAG 71 PLAUR NM_00265 CCCATGGATGCTCCTCTGAAGAGACTTTCCTCATTGACTGCCGAGGCCCCATGAATCAATGTCTGGTAGCCACCGG 72 PPARG NM_00503 TGACTTTATGGAGCCCAAGTTTGAGTTTGCTGTGAAGTTCAATGCACTGGAATTAGATGACAGCGACTTGGC 73 PTPD1 NM_00703 CGCTTGCCTAACTCATACTTTCCCGTTGACACTTGATCCACGCAGCGTGGCACTGGGACGTAAGTGGCGCAGTCTGA 74 ATGG RANBP2 NM_00626 TCCTTCAGCTTTCACACTGGGCTCAGAAATGAAGTTGCATGACTCTTCTGGAAGTCAGGTGGGAACAGGATTT 75 RASSF1 NM_00718 AGTGGGAGACACCTGACCTTTCTCAAGGTGAGATTGAGCAGAAGATCAAGGAGTACAATGCCCAGATCA 76 RB1 NM_00032 CGAAGCCCTTACAAGTTTCCTAGTTCACCCTTACGGATTCCTGGAGGGAACATGTATATTTCACCCCTGAAGAGTCC 77 RIZ1 NM_01223 CCAGACGAGCGATTAGAAGCGGCAGCTTGTGAGGTGAATGATTTGGGGGAAGAGGAGGAGGAGGAAGAGGAGGA 78 RPLPO NM_00100 CCATTCTATCATCAACGGGTACAAACGAGTCCTGGCCTTGTCTGTGGAGACGGATTACACCTTCCCACTTGCTGA 79 SPRY2 NM_00584 TGTGGCAAGTGCAAATGTAAGGAGTGCACCTACCCAAGGCCTCTGCCATCAGACTGGATCTGCGAC 80 Src NM_00438 CCTGAACATGAACATGAAGGAGCTGAAGCTGCTGCAGACCATCGGGAAGGGGGAGTTCGGAGACGTGATG 81 STK15 NM_00360 CATCTTCCAGGAGGACCACTCTCTGTGGCACCCTGGACTACCTGCCCCCTGAAATGATTGAAGGTCGGA 82 SURV NM_00116 TGTTTTGATTCCCGGGCTTACCAGGTGAGAAGTGAGGGAGGAAGAAGGCAGTGTCCCTTTTGCTAGAGCTGACAGCT 83 TTG TERC U86046 AAGAGGAACGGAGCGAGTCCCCGCGCGCGGCGCGATTCCCTGAGCTGTGGGACGTGCACCCAGGACTCGGCTCACAC 84 AT TFRC NM_00323 GCCAACTGCTTTCATTTGTGAGGGATCTGAACCAATACAGAGCAGACATAAAGGAAATGGGCCTGAGT 85 TGFBR2 NM_00324 AACACCAATGGGTTCCATCTTTCTGGGCTCCTGATTGCTCAAGCACAGTTTGGCCTGATGAAGAGG 86 TIMP2 NM_00325 TCACCCTCTGTGACTTCATCGTGCCCTGGGACACCCTGAGCACCACCCAGAAGAAGAGCCTGAACCACA 87 TITF1 NM_00331 CGACTCCGTTCTCAGTGTCTGACATCTTGAGTCCCCTGGAGGAAAGCTACAAGAAAGTGGGCATGGAGGG 88 TP53BP1 NM_00565 TGCTGTTGCTGAGTCTGTTGCCAGTCGCCAGAAGACGATGTCTGTGTTGAGCTGTATCTGTGAAGCCAGGCAAG 89 upa NM_00265 GTGGATGTGCCCTGAAGGACAAGCCAGGCGTCTACACGAGAGTCTCACACTTCTTACCCTGGATCCGCAG 90 VEGFC NM_00542 CCTCAGCAAGACGTTATTTGAAATTACAGTGCCTCTCTCTCAAGGCCCCAAACCAGTAACAATCAGTTTTGCCAATC 91 ACACTT XIAP NM_00116 GCAGTTGGAAGACACAGGAAAGTATCCCCAAATTGCAGATTTATCAACGGCTTTTATCTTGAAAATAGTGCCACGCA 92 YB-1 NM_00455 AGACTGTGGAGTTTGATGTTGTTGAAGGAGAAAAGGGTGCGGAGGCAGCAAATGTTACAGGTCCTGGTGGTGTTCC 93

TABLES 6A-6F Gene Accession Name Sequence Length Seq ID. A-Catenin NM_001903 S2138/A-Cate.f2 CGTTCCGATCCTCTATACTGCAT 23 94 A-Catenin NM_001903 S2139/A-Cate.r2 AGGTCCCTGTTGGCCTTATAGG 22 95 A-Catenin NM_001903 S4725/A-Cate.p2 ATGCCTACAGCACCCTGATGTCGCA 25 96 AKT1 NM_005163 S0010/AKT1.f3 CGCTTCTATGGCGCTGAGAT 20 97 AKT1 NM_005163 S0012/AKT1.r3 TCCCGGTACACCACGTTCTT 20 98 AKT1 NM_005163 S4776/AKT1.p3 CAGCCCTGGACTACCTGCACTCGG 24 99 AKT2 NM_001626 S0828/AKT2.f3 TCCTGCCACCCTTCAAACC 19 100 AKT2 NM_001626 S0829/AKT2.r3 GGCGGTAAATTCATCATCGAA 21 101 AKT2 NM_001626 S4727/AKT2.p3 CAGGTCACGTCCGAGGTCGACACA 24 102 APC NM_000038 S0022/APC.f4 GGACAGCAGGAATGTGTTTC 20 103 APC NM_000038 S0024/APC.r4 ACCCACTCGATTTGTTTCTG 20 104 APC NM_000038 S4888/APC.p4 CATTGGCTCCCCGTGACCTGTA 22 105 B-Catenin NM_001904 S2150/B-Cate.f3 GGCTCTTGTGCGTACTGTCCTT 22 106 B-Catenin NM_001904 S2151/B-Cate.r3 TCAGATGACGAAGAGCACAGATG 23 107 B-Catenin NM_001904 S5046/B-Cate.p3 AGGCTCAGTGATGTCTTCCCTGTCACCAG 29 108 Bak NM_001188 S0037/Bak.f2 CCATTCCCACCATTCTACCT 20 109 Bak NM_001188 S0039/Bak.r2 GGGAACATAGACCCACCAAT 20 110 Bak NM_001188 S4724/Bak.p2 ACACCCCAGACGTCCTGGCCT 21 111 Bax NM_004324 S0040/Bax.f1 CCGCCGTGGACACAGACT 18 112 Bax NM_004324 S0042/Bax.r1 TTGCCGTCAGAAAACATGTCA 21 113 Bax NM_004324 S4897/Bax.p1 TGCCACTCGGAAAAAGACCTCTCGG 25 114 Bclx NM_001191 S0046/Bclx.f2 CTTTTGTGGAACTCTATGGGAACA 24 115 Bclx NM_001191 S0048/Bclx.r2 CAGCGGTTGAAGCGTTCCT 19 116 Bclx NM_001191 S4898/Bclx.p2 TTCGGCTCTCGGCTGCTGCA 20 117 BRAF NM_004333 S3027/BRAF.f2 CCTTCCGACCAGCAGATGAA 20 118 BRAF NM_004333 S3028/BRAF.r2 TTTATATGCACATTGGGAGCTGAT 24 119 BRAF NM_004333 S4818/BRAF.p2 CAATTTGGGCAACGAGACCGATCCT 25 120 BRK NM_005975 S0678/BRK.f2 GTGCAGGAAAGGTTCACAAA 20 121 BRK NM_005975 S0679/BRK.r2 GCACACACGATGGAGTAAGG 20 122 BRK NM_005975 S4789/BRK.p2 AGTGTCTGCGTCCAATACACGCGT 24 123 BTC NM_001729 S1216/BTC.f3 AGGGAGATGCCGCTTCGT 18 124 BTC NM_001729 S1217/BTC.r3 CTCTCACACCTTGCTCCAATGTA 23 125 BTC NM_001729 S4844/BTC.p3 CCTTCATCACAGACACAGGAGGGCG 25 126 CA9 NM_001216 S1398/CA9.f3 ATCCTAGCCCTGGTTTTTGG 20 127 CA9 NM_001216 S1399/CA9.r3 CTGCCTTCTCATCTGCACAA 20 128 CA9 NM_001216 S4938/CA9.p3 TTTGCTGTCACCAGCGTCGC 20 129 Cad17 NM_004063 S2186/Cad17.f1 GAAGGCCAAGAACCGAGTCA 20 130 Cad17 NM_004063 S2187/Cad17.r1 TCCCCAGTTAGTTCAAAAGTCACA 24 131 Cad17 NM_004063 S5038/Cad17.p1 TTATATTCCAGTTTAAGGCCAATCCTC 27 132 CCNA2 NM_001237 S3039/CCNA2.f1 CCATACCTCAAGTATTTGCCATCAG 25 133 CCNA2 NM_001237 S3040/CCNA2.r1 AGCTTTGTCCCGTGACTGTGTA 22 134 CCNA2 NM_001237 S4820/CCNA2.p1 ATTGCTGGAGCTGCCTTTCATTTAGCACT 29 135 CCND3 NM_001760 S2799/CCND3.f1 CCTCTGTGCTACAGATTATACCTTTGC 27 136 CCND3 NM_001760 S2800/CCND3.r1 CACTGCAGCCCCAATGCT 18 137 CCND3 NM_001760 S4966/CCND3.p1 TACCCGCCATCCATGATCGCCA 22 138 CCNE1 NM_001238 S1446/CCNE1.f1 AAAGAAGATGATGACCGGGTTTAC 24 139 CCNE1 NM_001238 S1447/CCNE1.r1 GAGCCTCTGGATGGTGCAAT 20 140 CCNE1 NM_001238 S4944/CCNE1.p1 CAAACTCAACGTGCAAGCCTCGGA 24 141 CCNE2 NM_057749 S1458/CCNE2.f2 ATGCTGTGGCTCCTTCCTAACT 22 142 CCNE2 NM_057749 S1459/CCNE2.r2 ACCCAAATTGTGATATACAAAAAGGTT 27 143 CCNE2 NM_057749 S4945/CCNE2.p2 TACCAAGCAACCTACATGTCAAGAAAGCCC 30 144 CD105 NM_000118 S1410/CD105.f1 GCAGGTGTCAGCAAGTATGATCAG 24 145 CD105 NM_000118 S1411/CD105.r1 TTTTTCCGCTGTGGTGATGA 20 146 CD105 NM_000118 S4940/CD105.p1 CGACAGGATATTGACCACCGCCTCATT 27 147 CD134 NM_003327 S3138/CD134.f2 GCCCAGTGCGGAGAACAG 18 148 CD134 NM_003327 S3139/CD134.r2 AATCACACGCACCTGGAGAAC 21 149 CD134 NM_003327 S3241/CD134.p2 CCAGCTTGATTCTCGTCTCTGCACTTAAGC 30 150 CD44E X55150 S3267/CD44E.f1 ATCACCGACAGCACAGACA 19 151 CD44E X55150 S3268/CD44E.r1 ACCTGTGTTTGGATTTGCAG 20 152 CD44E X55150 S4767/CD44E.p1 CCCTGCTACCAATATGGACTCCAGTCA 27 153 CD44s M59040 S3102/CD44s.f1 GACGAAGACAGTCCCTGGAT 20 154 CD44s M59040 S3103/CD44s.r1 ACTGGGGTGGAATGTGTCTT 20 155 CD44s M59040 S4826/CD44s.p1 CACCGACAGCACAGACAGAATCCC 24 156 CD44v3 AJ251595v3 S2997/CD44v3.f2 CACACAAAACAGAACCAGGACT 22 157 CD44v3 AJ251595v3 S2998/CD44v3.r2 CTGAAGTAGCACTTCCGGATT 21 157 CD44v3 AJ251595v3 S4814/CD44v3.p2 ACCCAGTGGAACCCAAGCCATTC 23 159 CD44v6 AJ251595v6 S3003/CD44v6.f1 CTCATACCAGCCATCCAATG 20 160 CD44v6 AJ251595v6 S3004/CD44v6.r1 TTGGGTTGAAGAAATCAGTCC 21 161 CD44v6 AJ251595v6 S4815/CD44v6.p1 CACCAAGCCCAGAGGACAGTTCCT 24 162 CD68 NM_001251 S0067/CD68.f2 TGGTTCCCAGCCCTGTGT 18 163 CD68 NM_001251 S0069/CD68.r2 CTCCTCCACCCTGGGTTGT 19 164 CD68 NM_001251 S4734/CD68.p2 CTCCAAGCCCAGATTCAGATTCGAGTCA 28 165 CD82 NM_002231 S0684/CD82.f3 GTGCAGGCTCAGGTGAAGTG 20 166 CD82 NM_002231 S0685/CD82.r3 GACCTCAGGGCGATTCATGA 20 167 CD82 NM_002231 S4790/CD82.p3 TCAGCTTCTACAACTGGACAGACAACGCTG 30 168 CD9 NM_001769 S0686/CD9.f1 GGGCGTGGAACAGTTTATCT 20 168 CD9 NM_001769 S0687/CD9.r1 CACGGTGAAGGTTTCGAGT 19 170 CD9 NM_001769 S4792/CD9.p1 AGACATCTGCCCCAAGAAGGACGT 24 171 CDC25B NM_021874 S1160/CDC25B.f1 AAACGAGCAGTTTGCCATCAG 21 172 CDC25B NM_021874 S1161/CDC25B.r1 GTTGGTGATGTTCCGAAGCA 20 176 CDC25B NM_021874 S4842/CDC25B.p1 CCTCACCGGCATAGACTGGAAGCG 24 174 CEACAM6 NM_002483 S3197/CEACAM.f1 CACAGCCTCACTTCTAACCTTCTG 24 175 CEACAM6 NM_002483 S3198/CEACAM.r1 TTGAATGGCGTGGATTCAATAG 22 176 CEACAM6 NM_002483 S3261/CEACAM.p1 ACCCACCCACCACTGCCAAGCTC 23 177 CGA NM_001275 S3221/CGA.f3 CTGAAGGAGCTCCAAGACCT 20 178 CGA NM_001275 S3222/CGA.r3 CAAAACCGCTGTGTTTCTTC 20 179 CGA NM_001275 S3254/CGA.p3 TGCTGATGTGCCCTCTCCTTGG 22 180 Chk2 NM_007194 S1434/Chk2.f3 ATGTGGAACCCCCACCTACTT 21 181 Chk2 NM_007194 S1435/Chk2.r3 CAGTCCACAGCACGGTTATACC 22 182 Chk2 NM_007194 S4942/Chk2.p3 AGTCCCAACAGAAACAAGAACTTCAGGCG 29 183 cMet NM_000245 S0082/cMet.f2 ACATTTCCAGTCCTGCAGTCA 22 184 cMet NM_000245 S0084/cMet.r2 CTCCGATCGCACACATTTGT 20 185 cMet NM_000245 S4993/cMet.p2 TGCCTCTCTGCCCCACCCTTTGT 23 186 COX2 NM_000963 S0088/COX2.f1 TCTGCAGAGTTGGAAGCACTCTA 23 187 COX2 NM_000963 S0090/COX2.r1 GCCGAGGCTTTTCTACCAGAA 21 188 COX2 NM_000963 S4995/COX2.p1 CAGGATACAGCTCCACAGCATCGATGTC 28 189 cripto NM_003212 S3117/cripto.f1 GGGTCTGTGCCCCATGAC 18 190 cripto NM_003212 S3118/cripto.r1 TGACCGTGCCAGCATTTACA 20 191 cripto NM_003212 S3237/cripto.p1 CCTGGCTGCCCAAGAAGTGTTCCCT 25 192 CTSL NM_001912 S1303/CTSL.f2 GGGAGGCTTATCTCACTGAGTGA 23 193 CTSL NM_001912 S1304/CTSL.r2 CCATTGCAGCCTTCATTGC 19 194 CTSL NM_001912 S4899/CTSL.p2 TTGAGGCCCAGAGCAGTCTACCAGATTCT 29 195 DCR3 NM_016434 S1786/DCR3.f3 GACCAAGGTCCTGGAATGTC 20 196 DCR3 NM_016434 S1787/DCR3.r3 GTCTTCCCTGTACCCGTAGG 20 197 DCR3 NM_016434 S4982/DCR3.p3 CAGGATGCCATTCACCTTCTGCTG 24 198 DIABLO NM_019887 S0808/DIABLO.f1 CACAATGGCGGCTCTGAAG 19 199 DIABLO NM_019887 S0809/DIABLO.r1 ACACAAACACTGTCTGTACCTGAAGA 26 200 DIABLO NM_019887 S4813/DIABLO.p1 AAGTTACGCTGCGCGACAGCCAA 23 201 DPYD NM_000110 S0100/DPYD.f2 AGGACGCAAGGAGGGTTTG 19 202 DPYD NM_000110 S0102/DPYD.r2 GATGTCCGCCGAGTCCTTACT 21 203 DPYD NM_000110 S4998/DPYD.p2 CAGTGCCTACAGTCTCGAGTCTGCCAGTG 29 204 DR5 NM_003842 S2551/DR5.f2 CTCTGAGACAGTGCTTCGATGACT 24 205 DR5 NM_003842 S2552/DR5.r2 CCATGAGGCCCAACTTCCT 19 206 DR5 NM_003842 S4979/DR5.p2 CAGACTTGGTGCCCTTTGACTCC 23 207 EDN1 NM_001955 S0774/EDN1 e.f1 TGCCACCTGGACATCATTTG 20 208 endothelin EDN1 NM_001955 S0775/EDN1 e.r1 TGGACCTAGGGCTTCCAAGTC 21 209 endothelin EDN1 NM_001955 S4806/EDN1 e.p1 CACTCCCGAGCACGTTGTTCCGT 23 210 endothelin EGFR NM_005228 S0103/EGFR.f2 TGTCGATGGACTTCCAGAAC 20 211 EGFR NM_005228 S0105/EGFR.r2 ATTGGGACAGCTTGGATCA 19 212 EGFR NM_005228 S4999/EGFR.p2 CACCTGGGCAGCTGCCAA 18 213 EGFRd27 EGFRd27 S2484/EGFRd2.f2 GAGTCGGGCTCTGGAGGAAAAG 22 214 EGFRd27 EGFRd27 S2485/EGFRd2.r2 CCACAGGCTCGGACGCAC 18 215 EGFRd27 EGFRd27 S4935/EGFRd2.p2 AGCCGTGATCTGTCACCACATAATTACC 28 216 EIF4E NM_001968 S0106/EIF4E.f1 GATCTAAGATGGCGACTGTCGAA 23 217 EIF4E NM_001968 S0108/EIF4E.r1 TTAGATTCCGTTTTCTCCTCTTCTG 25 218 EIF4E NM_001968 S5000/EIF4E.p1 ACCACCCCTACTCCTAATCCCCCGACT 27 219 ErbB3 NM_001982 S0112/ErbB3.f1 CGGTTATGTCATGCCAGATACAC 23 220 ErbB3 NM_001982 S0114/ErbB3.r1 GAACTGAGACCCACTGAAGAAAGG 24 221 ErbB3 NM_001982 S5002/ErbB3.p1 CCTCAAAGGTACTCCCTCCTCCCGG 25 222 ERBB4 NM_005235 S1231/ERBB4.f3 TGGCTCTTAATCAGTTTCGTTACCT 25 223 ERBB4 NM_005235 S1232/ERBB4.r3 CAAGGCATATCGATCCTCATAAAGT 25 224 ERBB4 NM_005235 S4891/ERBB4.p3 TGTCCCACGAATAATGCGTAAATTCTCCAG 30 225 EREG NM_001432 S0670/EREG.f1 ATAACAAAGTGTAGCTCTGACATGAATG 28 226 EREG NM_001432 S0671/EREG.r1 CACACCTGCAGTAGTTTTGACTCA 24 227 EREG NM_001432 S4772/EREG.p1 TTGTTTGCATGGACAGTGCATCTATCTGGT 30 228 ERK1 Z11696 S1560/ERK1.f3 ACGGATCACAGTGGAGGAAG 20 229 ERK1 Z11696 S1561/ERK1.r3 CTCATCCGTCGGGTCATAGT 20 230 ERK1 Z11696 S4882/ERK1.p3 CGCTGGCTCACCCCTACCTG 20 231 fas NM_000043 S0118/fas.f1 GGATTGCTCAACAACCATGCT 21 232 fas NM_000043 S0120/fas.r1 GGCATTAACACTTTTGGACGATAA 24 233 fas NM_000043 S5003/fas.p1 TCTGGACCCTCCTACCTCTGGTTCTTACGT 30 234 FRP1 NM_003012 S1804/FRP1.f3 TTGGTACCTGTGGGTTAGCA 20 235 FRP1 NM_003012 S1805/FRP1.r3 CACATCCAAATGCAAACTGG 20 236 FRP1 NM_003012 S4983/FRP1.p3 TCCCCAGGGTAGAATTCAATCAGAGC 26 237 GPC3 NM_004484 S1835/GPC3.f1 TGATGCGCCTGGAAACAGT 19 238 GPC3 NM_004484 S1836/GPC3.r1 CGAGGTTGTGAAAGGTGCTTATC 23 239 GPC3 NM_004484 S5036/GPC3.p1 AGCAGGCAACTCCGAAGGACAACG 24 240 GRO1 NM_001511 S0133/GRO1.f2 CGAAAAGATGCTGAACAGTGACA 23 241 GRO1 NM_001511 S0135/GRO1.r2 TCAGGAACAGCCACCAGTGA 20 242 GRO1 NM_001511 S5006/GRO1.p2 CTTCCTCCTCCCTTCTGGTCAGTTGGAT 28 243 GUS NM_000181 S0139/GUS.f1 CCCACTCAGTAGCCAAGTCA 20 244 GUS NM_000181 S0141/GUS.r1 CACGCAGGTGGTATCAGTCT 20 245 GUS NM_000181 S4740/GUS.p1 TCAAGTAAACGGGCTGTTTTCCAAACA 27 246 HB-EGF NM_001945 S0662/HB-EGF.f1 GACTCCTTCGTCCCCAGTTG 20 247 HB-EGF NM_001945 S0663/HB-EGF.r1 TGGCACTTGAAGGCTCTGGTA 21 248 HB-EGF NM_001945 S4787/HB-EGF.p1 TTGGGCCTCCCATAATTGCTTTGCC 25 249 HER2 NM_004448 S0142/HER2.f3 CGGTGTGAGAAGTGCAGCAA 20 250 HER2 NM_004448 S0144/HER2.r3 CCTCTCGCAAGTGCTCCAT 19 251 HER2 NM_004448 S4729/HER2.p3 CCAGACCATAGCACACTCGGGCAC 24 242 HGF M29145 S1327/HGF.f4 CCGAAATCCAGATGATGATG 20 253 HGF M29145 S1328/HGF.r4 CCCAAGGAATGAGTGGATTT 20 254 HGF M29145 S4901/HGF.p4 CTCATGGACCCTGGTGCTACACG 23 255 ID1 NM_002165 S0820/ID1.f1 AGAACCGCAAGGTGAGCAA 19 256 ID1 NM_002165 S0821/ID1.r1 TCCAACTGAAGGTCCCTGATG 21 257 ID1 NM_002165 S4832/ID1.p1 TGGAGATTCTCCAGCACGTCATCGAC 26 258 IGF1R NM_000875 S1249/IGF1R.f3 GCATGGTAGCCGAAGATTTCA 21 259 IGF1R NM_000875 S1250/IGF1R.r3 TTTCCGGTAATAGTCTGTCTCATAGATATC 30 260 IGF1R NM_000875 S4895/IGF1R.p3 CGCGTCATACCAAAATCTCCGATTTTGA 28 261 IGFBP3 NM_000598 S0157/IGFBP3.f3 ACGCACCGGGTGTCTGA 17 262 IGFBP3 NM_000598 S0159/IGFBP3.r3 TGCCCTTTCTTGATGATGATTATC 24 263 IGFBP3 NM_000598 S5011/IGFBP3.p3 CCCAAGTTCCACCCCCTCCATTCA 24 264 IRS1 NM_005544 S1943/IRS1.f3 CCACAGCTCACCTTCTGTCA 20 265 IRS1 NM_005544 S1944/IRS1.r3 CCTCAGTGCCAGTGTCTTCC 20 266 IRS1 NM_005544 S5050/IRS1.p3 TCCATCCCAGCTCCAGCCAG 20 267 ITGA3 NM_002204 S2347/ITGA3.f2 CCATGATCCTCACTCTGCTG 20 268 ITGA3 NM_002204 S2348/ITGA3.r2 GAAGCTTTGTAGCCGGTGAT 20 269 ITGA3 NM_002204 S4852/ITGA3.p2 CACTCCAGACCTCGCTTAGCATGG 24 270 ITGB3 NM_000212 S3126/ITGB3.f1 ACCGGGAGCCCTACATGAC 19 271 ITGB3 NM_000212 S3127/ITGB3.r1 CCTTAAGCTCTTTCACTGACTCAATCT 27 272 ITGB3 NM_000212 S3243/ITGB3.p1 AAATACCTGCAACCGTTACTGCCGTGAC 28 273 KRT17 NM_000422 S0172/KRT17.f2 CGAGGATTGGTTCTTCAGCAA 21 274 KRT17 NM_000422 S0174/KRT17.r2 ACTCTGCACCAGCTCACTGTTG 22 275 KRT17 NM_000422 S5013/KRT17.p2 CACCTCGCGGTTCAGTTCCTCTGT 24 276 LAMC2 NM_005562 S2826/LAMC2.f2 ACTCAAGCGGAAATTGAAGCA 21 277 LAMC2 NM_005562 S2827/LAMC2.r2 ACTCCCTGAAGCCGAGACACT 21 278 LAMC2 NM_005562 S4969/LAMC2.p2 AGGTCTTATCAGCACAGTCTCCGCCTCC 28 278 MTA1 NM_004689 S2369/MTA1.f1 CCGCCCTCACCTGAAGAGA 19 280 MTA1 NM_004689 S2370/MTA1.r1 GGAATAAGTTAGCCGCGCTTCT 22 281 MTA1 NM_004689 S4855/MTA1.p1 CCCAGTGTCCGCCAAGGAGCG 21 282 NMYC NM_005378 S2884/NMYC.f2 TGAGCGTCGCAGAAACCA 18 283 NMYC NM_005378 S2885/NMYC.r2 TCCCTGAGCGTGAGAAAGCT 20 284 NMYC NM_005378 S4976/NMYC.p2 CCAGCGCCGCAACGACCTTC 20 285 p14ARF NM_000077 S0199/p14ARF.f3 GCGGAAGGTCCCTCAGACA 19 286 p14ARF NM_000077 S0201/p14ARF.r3 TCTAAGTTTCCCGAGGTTTCTCA 23 297 p14ARF NM_000077 S5068/p14ARF.p3 CCCCGATTGAAAGAACCAGAGAGGCT 26 288 p27 NM_004064 S0205/p27.f3 CGGTGGACCACGAAGAGTTAA 21 289 p27 NM_004064 S0207/p27.r3 GGCTCGCCTCTTCCATGTC 19 290 p27 NM_004064 S4750/p27.p3 CCGGGACTTGGAGAAGCACTGCA 23 291 P53 NM_000546 S0208/P53.f2 CTTTGAACCCTTGCTTGCAA 20 292 P53 NM_000546 S0210/P53.r2 CCCGGGACAAAGCAAATG 18 293 P53 NM_000546 S5065/P53.p2 AAGTCCTGGGTGCTTCTGACGCACA 25 294 PAI1 NM_000602 S0211/PAI1.f3 CCGCAACGTGGTTTTCTCA 19 295 PAI1 NM_000602 S0213/PAI1.r3 TGCTGGGTTTCTCCTCCTGTT 21 296 PAI1 NM_000602 S5066/PAI1.p3 CTCGGTGTTGGCCATGCTCCAG 22 297 PDGFA NM_002607 S0214/PDGFA.f3 TTGTTGGTGTGCCCTGGTG 19 298 PDGFA NM_002607 S0216/PDGFA.r3 TGGGTTCTGTCCAAACACTGG 21 299 PDGFA NM_002607 S5067/PDGFA.p3 TGGTGGCGGTCACTCCCTCTGC 22 300 PDGFB NM_002608 S0217/PDGFB.f3 ACTGAAGGAGACCCTTGGAG 20 301 PDGFB NM_002608 S0219/PDGFB.r3 TAAATAACCCTGCCCACACA 20 302 PDGFB NM_002608 S5014/PDGFB.p3 TCTCCTGCCGATGCCCCTAGG 21 303 PGK1 NM_000291 S0232/PGK1.f1 AGAGCCAGTTGCTGTAGAACTCAA 24 304 PGK1 NM_000291 S0234/PGK1.r1 CTGGGCCTACACAGTCCTTCA 21 305 PGK1 NM_000291 S5022/PGK1.p1 TCTCTGCTGGGCAAGGATGTTCTGTTC 27 306 PLAUR NM_002659 S1976/PLAUR.f3 CCCATGGATGCTCCTCTGAA 20 307 PLAUR NM_002659 S1977/PLAUR.r3 CCGGTGGCTACCAGACATTG 20 308 PLAUR NM_002659 S5054/PLAUR.p3 CATTGACTGCCGAGGCCCCATG 22 309 PPARG NM_005037 S3090/PPARG.f3 TGACTTTATGGAGCCCAAGTT 21 310 PPARG NM_005037 S3091/PPARG.r3 GCCAAGTCGCTGTCATCTAA 20 311 PPARG NM_005037 S4824/PPARG.p3 TTCCAGTGCATTGAACTTCACAGCA 25 312 PTPD1 NM_007039 S3069/PTPD1.f2 CGCTTGCCTAACTCATACTTTCC 23 313 PTPD1 NM_007039 S3070/PTPD1.r2 CCATTCAGACTGCGCCACTT 20 314 PTPD1 NM_007039 S4822/PTPD1.p2 TCCACGCAGCGTGGCACTG 19 315 RANBP2 NM_006267 S3081/RANBP2.f3 TCCTTCAGCTTTCACACTGG 20 316 RANBP2 NM_006267 S3082/RANBP2.r3 AAATCCTGTTCCCACCTGAC 20 317 RANBP2 NM_006267 S4823/RANBP2.p3 TCCAGAAGAGTCATGCAACTTCATTTCTG 29 318 RASSF1 NM_007182 S2393/RASSF1.f3 AGTGGGAGACACCTGACCTT 20 319 RASSF1 NM_007182 S2394/RASSF1.r3 TGATCTGGGCATTGTACTGC 20 320 RASSF1 NM_007182 S4909/RASSF1.p3 TTGATCTTCTGCTCAATCTCAGCTTGAGA 29 321 RB1 NM_000321 S2700/RB1.f1 CGAAGCCCTTACAAGTTTCC 20 322 RB1 NM_000321 S2701/RB1.r1 GGACTCTTCAGGGGTGAAAT 20 323 RB1 NM_000321 S4765/RB1.p1 CCCTTACGGATTCCTGGAGGGAAC 24 324 RIZ1 NM_012231 S1320/RIZ1.f2 CCAGACGAGCGATTAGAAGC 20 325 RIZ1 NM_012231 S1321/RIZ1.r2 TCCTCCTCTTCCTCCTCCTC 20 326 RIZ1 NM_012231 S4761/RIZ1.p2 TGTGAGGTGAATGATTTGGGGGA 23 327 RPLPO NM_001002 S0256/RPLPO.f2 CCATTCTATCATCAACGGGTACAA 24 328 RPLPO NM_001002 S0258/RPLPO.r2 TCAGCAAGTGGGAAGGTGTAATC 23 329 RPLPO NM_001002 S4744/RPLPO.p2 TCTCCACAGACAAGGCCAGGACTCG 25 330 SPRY2 NM_005842 S2985/SPRY2.f2 TGTGGCAAGTGCAAATGTAA 20 331 SPRY2 NM_005842 S2986/SPRY2.r2 GTCGCAGATCCAGTCTGATG 20 332 SPRY2 NM_005842 S4811/SPRY2.p2 CAGAGGCCTTGGGTAGGTGCACTC 24 333 Src NM_004383 S1820/Src.f2 CCTGAACATGAAGGAGCTGA 20 334 Src NM_004383 S1821/Src.r2 CATCACGTCTCCGAACTCC 19 335 Src NM_004383 S5034/Src.p2 TCCCGATGGTCTGCAGCAGCT 21 336 STK15 NM_003600 S0794/STK15.f2 CATCTTCCAGGAGGACCACT 20 337 STK15 NM_003600 S0795/STK15.r2 TCCGACCTTCAATCATTTCA 20 338 STK15 NM_003600 S4745/STK15.p2 CTCTGTGGCACCCTGGACTACCTG 24 339 SURV NM_001168 S0259/SURV.f2 TGTTTTGATTCCCGGGCTTA 20 340 SURV NM_001168 S0261/SURV.r2 CAAAGCTGTCAGCTCTAGCAAAAG 24 341 SURV NM_001168 S4747/SURV.p2 TGCCTTCTTCCTCCCTCACTTCTCACCT 28 342 TERC U86046 S2709/TERC.f2 AAGAGGAACGGAGCGAGTC 19 343 TERC U86046 S2710/TERC.r2 ATGTGTGAGCCGAGTCCTG 19 344 TERC U86046 S4958/TERC.p2 CACGTCCCACAGCTCAGGGAATC 23 345 TFRC NM_003234 S1352/TFRC.f3 GCCAACTGCTTTCATTTGTG 20 346 TFRC NM_003234 S1353/TFRC.r3 ACTCAGGCCCATTTCCTTTA 20 347 TFRC NM_003234 S4748/TFRC.p3 AGGGATCTGAACCAATACAGAGCAGACA 28 348 TGFBR2 NM_003242 S2422/TGFBR2.f3 AACACCAATGGGTTCCATCT 20 349 TGFBR2 NM_003242 S2423/TGFBR2.r3 CCTCTTCATCAGGCCAAACT 20 350 TGFBR2 NM_003242 S4913/TGFBR2.p3 TTCTGGGCTCCTGATTGCTCAAGC 24 351 TIMP2 NM_003255 S1680/TIMP2.f1 TCACCCTCTGTGACTTCATCGT 22 352 TIMP2 NM_003255 S1681/TIMP2.r1 TGTGGTTCAGGCTCTTCTTCTG 22 353 TIMP2 NM_003255 S4916/TIMP2.p1 CCCTGGGACACCCTGAGCACCA 22 354 TITF1 NM_003317 S2224/TITF1.f1 CGACTCCGTTCTCAGTGTCTGA 22 355 TITF1 NM_003317 S2225/TITF1.r1 CCCTCCATGCCCACTTTCT 19 356 TITF1 NM_003317 S4829/TITF1.p1 ATCTTGAGTCCCCTGGAGGAAAGC 24 357 TP53BP1 NM_005657 S1747/TP53BP.f2 TGCTGTTGCTGAGTCTGTTG 20 358 TP53BP1 NM_005657 S1748/TP53BP.r2 CTTGCCTGGCTTCACAGATA 20 359 TP53BP1 NM_005657 S4924/TP53BP.p2 CCAGTCCCCAGAAGACCATGTCTG 24 360 upa NM_002658 S0283/upa.f3 GTGGATGTGCCCTGAAGGA 19 361 upa NM_002658 S0285/upa.r3 CTGCGGATCCAGGGTAAGAA 20 362 upa NM_002658 S4769/upa.p3 AAGCCAGGCGTCTACACGAGAGTCTCAC 28 363 VEGFC NM_005429 S2251/VEGFC.f1 CCTCAGCAAGACGTTATTTGAAATT 25 364 VEGFC NM_005429 S2252/VEGFC.r1 AAGTGTGATTGGCAAAACTGATTG 24 365 VEGFC NM_005429 S4758/VEGFC.p1 CCTCTCTCTCAAGGCCCCAAACCAGT 26 366 XIAP NM_001167 S0289/XIAP.f1 GCAGTTGGAAGACACAGGAAAGT 23 367 XIAP NM_001167 S0291/XIAP.r1 TGCGTGGCACTATTTTCAAGA 21 368 XIAP NM_001167 S4752/XIAP.p1 TCCCCAAATTGCAGATTTATCAACGGC 27 369 YB-1 NM_004559 S1194/YB-1.f2 AGACTGTGGAGTTTGATGTTGTTGA 25 370 YB-1 NM_004559 S1195/YB-1.r2 GGAACACCACCAGGACCTGTAA 22 371 YB-1 NM_004559 S4843/YB-1.p2 TTGCTGCCTCCGCACCCTTTTCT 23 372 

1. A prognostic method comprising: (a) subjecting a sample comprising EGFR-expressing cancer cells obtained from a patient to quantitative analysis of the expression level of the RNA transcript of at least one gene selected from the group consisting of CD44v3; CD44v6; DR5; GRO1; KRT17; and LAMC2, or their product, and (b) identifying the patient as likely to show resistance to treatment with an EGFR-inhibitor if the normalized expression levels of said gene or genes, or their products, are elevated above a defined expression threshold.
 2. The method of claim 1 wherein the patient is identified as likely to show resistance to treatment with an EGFR-inhibitor if the expression level of the RNA transcript of LAMC2 is elevated above a defined expression threshold.
 3. The method of claim 1 wherein the levels of the RNA transcripts of said genes are normalized relative to the mean level of the RNA transcript or the product of two or more housekeeping genes.
 4. The method of claim 3 wherein the housekeeping genes are selected from the group consisting of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Cypl, albumin, actins, tubulins, cyclophilin hypoxantine phosphoribosyltransferase (HRPT), L32, 28S, and 18S.
 5. The method of claim 3 wherein the sample is subjected to global gene expression analysis of all genes present above the limit of detection.
 6. The method of claim 5 wherein the levels of the RNA transcripts of said genes are normalized relative to the mean signal of the RNA transcripts or the products of all assayed genes or a subset thereof.
 7. The method of claim 6 wherein the level of RNA transcripts is determined by quantitative RT-PCR (qRT-PCR), and the signal is a Ct value.
 8. The method of claim 7 wherein the assayed genes include at least 50 cancer related genes.
 9. The method of claim 7 wherein the assayed genes includes at least 100 cancer related genes.
 10. The method of claim 1 wherein said patient is human.
 11. The method of claim 10 wherein said sample is a fixed, paraffin-embedded tissue (FPET) sample, or fresh or frozen tissue sample.
 12. The method of claim 10 wherein said sample is a tissue sample from fine needle, core, or other types of biopsy.
 13. The method of claim 10 wherein said quantitative analysis is performed by qRT-PCR.
 14. The method of claim 10 wherein said quantitative analysis is performed by quantifying the products of said genes.
 15. The method of claim 14 wherein said products are quantified by immunohistochemistry or by proteomics technology.
 16. The method of claim 10 wherein the EGFR-expressing cancer is selected from the group consisting of head and neck cancer, colon cancer, breast cancer, ovarian cancer, pancreatic cancer, and non-small cell lung carcinoma.
 17. The method of claim 16 wherein said cancer is head and neck cancer or colon cancer.
 18. The method of claim 10 further comprising the step of preparing a report comprising a statement whether the patient is likely to respond well to treatment with an EGFR inhibitor.
 19. The method of claim 10 further comprising the step of preparing a report comprising a statement whether the patient is likely to show resistance to treatment with an EGFR inhibitor.
 20. A method for predicting the likelihood that a patient diagnosed with an EGFR-expressing head or neck cancer will respond to treatment with an EGFR inhibitor, comprising determining the normalized level of one or more prognostic RNA transcripts or their products in a sample comprising EGFR-expressing cancer cells obtained from said patient, wherein the prognostic transcript is the transcript of one or more genes selected from the group consisting of: CD44s; CD82; CGA; CTSL; EGFRd27; IGFBP3; p27; P53; RB1; TIMP2; YB-1; A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CCNA2; CCNE1; CCNE2; CD105; CD44v3; CD44v6; CD68; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; PAI1; PDGFA; PGK1; PTPD1; RANBP2; SPRY2; TP53BP1; and VEGFC, wherein expression of one or more of A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CCNA2; CCNE1; CCNE2; CD105; CD44v3; CD44v6; CD68; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; PAI1; PDGFA; PGK1; PTPD1; RANBP2; SPRY2; TP53BP1; and VEGFC, or the corresponding gene product, above a defined threshold expression level indicates that the patient is likely to show resistance to treatment with an EGFR inhibitor, and expression of one or more of CD44s; CD82; CGA; CTSL; EGFRd27; IGFBP3; p27; P53; RB1; TIMP2; and YB-1, or the corresponding gene product, above a defined threshold expression level indicates that the patient is likely to respond well to treatment with an EGFR inhibitor.
 21. The method of claim 20 comprising determining the normalized levels of at least two of said prognostic transcripts or their expression products.
 22. The method of claim 20 comprising determining the normalized levels of at least 5 of said prognostic transcripts or their expression products.
 23. The method of claim 20 comprising determining the normalized levels of all of said prognostic transcripts or their expression products.
 24. The method of claim 20 wherein said sample is a tissue sample.
 25. The method of claim 24 wherein said tissue is fixed, paraffin-embedded, or fresh, or frozen.
 26. The method of claim 24 wherein said tissue is from fine needle, core, or other types of biopsy.
 27. The method of claim 20 further comprising the step of preparing a report comprising a statement whether said patient is likely to respond well to treatment with an EGFR inhibitor.
 28. The method of claim 20 further comprising the step of preparing a report comprising a statement whether said patient is likely to show resistance to treatment with an EGFR inhibitor.
 29. A method comprising treating a patient diagnosed with an EGFR-expressing head or neck cancer and determined to have elevated normalized expression of one or more of the RNA transcripts of CD44s; CD82; CGA; CTSL; EGFRd27; IGFBP3; p27; P53; RB1; TIMP2; and YB-1 genes, or the corresponding gene products in said cancer, or decreased normalized expression of one or more of the RNA transcripts of A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CCNA2; CCNE1; CCNE2; CD105; CD44v3; CD44v6; CD68; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; PAI1; PDGFA; PGK1; PTPD1; RANBP2; SPRY2; TP53BP1; and VEGFC genes, or the corresponding gene products in said cancer, with an effective amount of an EGFR-inhibitor, wherein, for each gene, elevated or decreased normalized expression is defined by a defined expression threshold value.
 30. The method of claim 29 wherein said patient has been determined to have elevated or decreased normalized expression of all of said RNA transcripts or the corresponding gene products.
 31. A method for predicting the likelihood that a patient diagnosed with an EGFR-expressing colon cancer will respond to treatment with an EGFR inhibitor, comprising determining the normalized level of one or more prognostic RNA transcripts or their products in a sample comprising EGFR-expressing cancer cells obtained from said patient, wherein the prognostic transcript is the transcript of one or more genes selected from the group consisting of: Bak; Bclx; BRAF; BRK; Cad17; CCND3; CCNE1; CCNE2; CD105; CD9; COX2; DIABLO; ErbB3; EREG; FRP1; GPC3; GUS; HER2; HGF; ID1; ITGB3; PTPD1; RPLPO; STK15; SURV; TERC; TGFBR2; TITF1; XIAP; CA9; CD134; CD44E; CD44v3; CD44v6; CDC25B; CGA; DR5; GRO1; KRT17; LAMC2; P14ARF; PDGFB; PLAUR; PPARG; RASSF1; RIZ1; Src; TFRC; and UPA, wherein the normalized level of one or more of CA9; CD134; CD44E; CD44v3; CD44v6; CDC25B; CGA; DR5; GRO1; KRT17; LAMC2; P14ARF; PDGFB; PLAUR; PPARG; RASSF1; RIZ1; Src; TFRC; and UPA, or the corresponding gene product, when above a defined expression threshold value, indicates that the patient is likely to show resistance to treatment with an EGFR inhibitor, and the normalized level of one or more of Bak; Bclx; BRAF; BRK; Cad17; CCND3; CCNE1; CCNE2; CD105; CD9; COX2; DIABLO; ErbB3; EREG; FRP1; GPC3; GUS; HER2; HGF; ID1; ITGB3; PTPD1; RPLPO; STK15; SURV; TERC; TGFBR2; TITF1; and XIAP, or the corresponding gene product, when above a defined expression threshold value, indicates that the patient is likely to respond well to treatment with an EGFR inhibitor.
 32. The method of claim 31 comprising determining the normalized levels of at least two of said prognostic transcripts or their expression products.
 33. The method of claim 31 comprising determining the normalized levels of at least 5 of said prognostic transcripts or their expression products.
 34. The method of claim 31 comprising determining the normalized levels of all of said prognostic transcripts or their expression products.
 35. The method of claim 31 wherein said sample is a tissue sample.
 36. The method of claim 35 wherein the tissue is fixed, paraffin-embedded, or fresh, or frozen.
 37. The method of claim 35 wherein the tissue is from fine needle, core, or other types of biopsy.
 38. The method of claim 31 further comprising the step of preparing a report comprising a statement whether the patient is likely to respond well to treatment with an EGFR inhibitor.
 39. The method of claim 31 further comprising the step of preparing a report comprising a statement whether the patient is likely to show resistance to treatment with an EGFR inhibitor.
 40. A method comprising treating a patient diagnosed with an EGFR-expressing colon cancer and determined to have elevated normalized expression of one or more of the RNA transcripts of Bak; Bclx; BRAF; BRK; Cad17; CCND3; CCNE1; CCNE2; CD105; CD9; COX2; DIABLO; ErbB3; EREG; FRP1; GPC3; GUS; HER2; HGF; ID1; ITGB3; PTPD 1; RPLPO; STK15; SURV; TERC; TGFBR2; TITF1; and XIAP genes, or the corresponding gene products in said cancer, or decreased normalized expression of one or more of the RNA transcripts of CA9; CD134; CD44E; CD44v3; CD44v6; CDC25B; CGA; DR5; GRO1; KRT17; LAMC2; P14ARF; PDGFB; PLAUR; PPARG; RASSF1; RIZ1; Src; TFRC; and UPA genes, or the corresponding gene products, with an effective amount of an EGFR-inhibitor, wherein for each gene elevated or decreased normalized expression is determined relative to a defined expression threshold.
 41. An array comprising polynucleotides hybridizing to the following genes: Bak; Bclx; BRAF; BRK; Cad17; CCND3; CD105; CD44s; CD82; CD9; CGA; CTSL; EGFRd27; ErbB3; EREG; GPC3; GUS; HGF; ID1; IGFBP3; ITGB3; ITGB3; p27; P53; PTPD1; RB1; RPLPO; STK15; SURV; TERC; TGFBR2; TIMP2; TITF1; XIAP; YB-1; A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CA9; CCNA2; CCNE1; CCNE2; CD134; CD44E; CD44v3; CD44v6; CD68; CDC25B; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; P14ARF; PAI1; PDGFA; PDGFB; PGK1; PLAUR; PPARG; RANBP2; RASSF1; RIZ1; SPRY2; Src; TFRC; TP53BP1; upa; and VEGFC, immobilized on a solid surface.
 42. The array of claim 41 wherein said polynucleotides are cDNAs.
 43. The array of claim 42 wherein said cDNAs are about 500 to about 5000 bases.
 44. The array of claim 41 wherein said polynucleotides are oligonucleotides.
 45. The array of claim 44 wherein said oligonucleotides are about 20 to 80 bases long.
 46. The array of claim 45 which comprises about 330,000 oligonucleotides.
 47. The array of claim 41 wherein said solid surface is glass.
 48. An array comprising polynucleotides hybridizing to the following genes: CD44v3; CD44v6; DR5; GRO1; KRT17; LAMC2.
 49. An array comprising polynucleotides hybridizing to the following genes: A-Catenin; AKT1; AKT2; APC; Bax; B-Catenin; BTC; CCNA2; CCNE1; CCNE2; CD105; CD44v3; CD44v6; CD68; CEACAM6; Chk2; cMet; COX2; cripto; DCR3; DIABLO; DPYD; DR5; EDN1 endothelin; EGFR; EIF4E; ERBB4; ERK1; fas; FRP1; GRO1; HB-EGF; HER2; IGF1R; IRS1; ITGA3; KRT17; LAMC2; MTA1; NMYC; PAI1; PDGFA; PGK1; PTPD1; RANBP2; SPRY2; TP53BP1; VEGFC; CD44s; CD82; CGA; CTSL; EGFRd27; IGFBP3; p27; P53; RB1; TIMP2; and YB-1.
 50. An array comprising polynucleotides hybridizing to the following genes: CA9; CD134; CD44E; CD44v3; CD44v6; CDC25B; CGA; DR5; GRO1; KRT17; LAMC2; P14ARF; PDGFB; PLAUR; PPARG; RASSF1; RIZ1; Src; TFRC; UPA; CD44s; CD82; CGA; CTSL; EGFRd27; IGFBP3; p27; P53; RB1; TIMP2; and YB-1.
 51. The method of any one of claims 1, 20 and 31, wherein RNA is isolated from said tissue by a procedure comprising: (a) incubating a section of said fixed, paraffin-embedded tissue specimen at a temperature of about 56° C. to 70° C. in a lysis buffer, in the presence of a protease, without prior dewaxing, to form a lysis solution; (b) cooling the lysis solution to a temperature where the wax solidifies; and (c) isolating the nucleic acid from said lysis solution.
 52. A kit comprising one or more of (1) extraction buffer/reagents and protocol; (2) reverse transcription buffer/reagents and protocol; and (3) qPCR buffer/reagents and protocol suitable for performing the method of any one of claims 1, 20 and
 30. 53. A method for amplification of a gene listed in Tables 5A and 5B by polymerase chain reaction (PCR), comprising performing said PCR by using an amplicon listed in Table 5A and 5B and a corresponding primer-probe set listed in Tables 6A-6F.
 54. A PCR primer-probe set listed in Tables 6A-6F.
 55. A PCR amplicon listed in Tables 5A and 5B. 